
import os,pandas as pd,numpy as np


os.chdir("D:\ML Project\data")


tele = pd.read_csv("telecom_churn_data.csv")


tele.head()


tele.shape

(99999, 227)


def char_min(row):
    min_of = np.min(row)
    return min_of


tele["min_arpu"]= tele[['arpu_6','arpu_7','arpu_8']].apply(char_min,axis=1)
tele["min_onnet_mou"]= tele[['onnet_mou_6','onnet_mou_7','onnet_mou_8']].apply(char_min,axis=1)
tele["min_ofnet_mou"]= tele[['offnet_mou_6','offnet_mou_7','offnet_mou_8']].apply(char_min,axis=1)
tele["min_roam_ic_mou"]= tele[['roam_ic_mou_6','roam_ic_mou_7','roam_ic_mou_8']].apply(char_min,axis=1)
tele["min_roam_og_mou"]= tele[['roam_og_mou_6','roam_og_mou_7','roam_og_mou_8']].apply(char_min,axis=1)
tele["min_loc_og_t2t_mou"]= tele[['loc_og_t2t_mou_6','loc_og_t2t_mou_7','loc_og_t2t_mou_8']].apply(char_min,axis=1)
tele["min_loc_og_t2m_mou"]= tele[['loc_og_t2m_mou_6','loc_og_t2m_mou_7','loc_og_t2m_mou_8']].apply(char_min,axis=1)
tele["min_loc_og_t2f_mou"]= tele[['loc_og_t2f_mou_6','loc_og_t2f_mou_7','loc_og_t2f_mou_8']].apply(char_min,axis=1)
tele["min_loc_og_t2c_mou"]= tele[['loc_og_t2c_mou_6','loc_og_t2c_mou_7','loc_og_t2c_mou_8']].apply(char_min,axis=1)
tele["min_loc_og_mou"]= tele[['loc_og_mou_6','loc_og_mou_7','loc_og_mou_8']].apply(char_min,axis=1)
tele["min_std_og_t2t_mou"]= tele[['std_og_t2t_mou_6','std_og_t2t_mou_7','std_og_t2t_mou_8']].apply(char_min,axis=1)
tele["min_std_og_t2m_mou"]= tele[['std_og_t2m_mou_6','std_og_t2m_mou_7','std_og_t2m_mou_8']].apply(char_min,axis=1)
tele["min_std_og_t2f_mou"]= tele[['std_og_t2f_mou_6','std_og_t2f_mou_7','std_og_t2f_mou_8']].apply(char_min,axis=1)
tele["min_std_og_t2c_mou"]= tele[['std_og_t2c_mou_6','std_og_t2c_mou_7','std_og_t2c_mou_8']].apply(char_min,axis=1)
tele["min_isd_og_mou"]= tele[['isd_og_mou_6','isd_og_mou_7','isd_og_mou_8']].apply(char_min,axis=1)
tele["min_std_og_mou"]= tele[['std_og_mou_6','std_og_mou_7','std_og_mou_8']].apply(char_min,axis=1)
tele["min_spl_og_mou"]= tele[['spl_og_mou_6','spl_og_mou_7','spl_og_mou_8']].apply(char_min,axis=1)
tele["min_og_others"]= tele[['og_others_6','og_others_7','og_others_8']].apply(char_min,axis=1)
tele["min_total_og_mou"]= tele[['total_og_mou_6','total_og_mou_7','total_og_mou_8']].apply(char_min,axis=1)
tele["min_loc_ic_t2t_mou"]= tele[['loc_ic_t2t_mou_6','loc_ic_t2t_mou_7','loc_ic_t2t_mou_8']].apply(char_min,axis=1)
tele["min_loc_ic_t2m_mou"]= tele[['loc_ic_t2m_mou_6','loc_ic_t2m_mou_7','loc_ic_t2m_mou_8']].apply(char_min,axis=1)
tele["min_loc_ic_t2f_mou"]= tele[['loc_ic_t2f_mou_6','loc_ic_t2f_mou_7','loc_ic_t2f_mou_8']].apply(char_min,axis=1)
tele["min_loc_ic_mou"]= tele[['loc_ic_mou_6','loc_ic_mou_7','loc_ic_mou_8']].apply(char_min,axis=1)
tele["min_std_ic_t2t_mou"]= tele[['std_ic_t2t_mou_6','std_ic_t2t_mou_7','std_ic_t2t_mou_8']].apply(char_min,axis=1)
tele["min_std_ic_t2m_mou"]= tele[['std_ic_t2m_mou_6','std_ic_t2m_mou_7','std_ic_t2m_mou_8']].apply(char_min,axis=1)
tele["min_std_ic_t2f_mou"]= tele[['std_ic_t2f_mou_6','std_ic_t2f_mou_7','std_ic_t2f_mou_8']].apply(char_min,axis=1)
tele["min_std_ic_t2o_mou"]= tele[['std_ic_t2o_mou_6','std_ic_t2o_mou_7','std_ic_t2o_mou_8']].apply(char_min,axis=1)
tele["min_std_ic_mou"]= tele[['std_ic_mou_6','std_ic_mou_7','std_ic_mou_8']].apply(char_min,axis=1)
tele["min_total_ic_mou"]= tele[['total_ic_mou_6','total_ic_mou_7','total_ic_mou_8']].apply(char_min,axis=1)
tele["min_spl_ic_mou"]= tele[['spl_ic_mou_6','spl_ic_mou_7','spl_ic_mou_8']].apply(char_min,axis=1)
tele["min_isd_ic_mou"]= tele[['isd_ic_mou_6','isd_ic_mou_7','isd_ic_mou_8']].apply(char_min,axis=1)
tele["min_ic_others"]= tele[['ic_others_6','ic_others_7','ic_others_8']].apply(char_min,axis=1)
tele["min_total_rech_num"]= tele[['total_rech_num_6','total_rech_num_7','total_rech_num_8']].apply(char_min,axis=1)
tele["min_total_rech_amt"]= tele[['total_rech_amt_6','total_rech_amt_7','total_rech_amt_8']].apply(char_min,axis=1)
tele["min_max_rech_amt"]= tele[['max_rech_amt_6','max_rech_amt_7','max_rech_amt_8']].apply(char_min,axis=1)
tele["min_last_day_rch_amt"]= tele[['last_day_rch_amt_6','last_day_rch_amt_7','last_day_rch_amt_8']].apply(char_min,axis=1)
tele["min_total_rech_data"]= tele[['total_rech_data_6','total_rech_data_7','total_rech_data_8']].apply(char_min,axis=1)
tele["min_max_rech_data"]= tele[['max_rech_data_6','max_rech_data_7','max_rech_data_8']].apply(char_min,axis=1)
tele["min_count_rech_2g"]= tele[['count_rech_2g_6','count_rech_2g_7','count_rech_2g_8']].apply(char_min,axis=1)
tele["min_count_rech_3g"]= tele[['count_rech_3g_6','count_rech_3g_7','count_rech_3g_8']].apply(char_min,axis=1)
tele["min_av_rech_amt_data"]= tele[['av_rech_amt_data_6','av_rech_amt_data_7','av_rech_amt_data_8']].apply(char_min,axis=1)
tele["min_vol_2g_mb"]= tele[['vol_2g_mb_6','vol_2g_mb_7','vol_2g_mb_8']].apply(char_min,axis=1)
tele["min_vol_3g_mb"]= tele[['vol_3g_mb_6','vol_3g_mb_7','vol_3g_mb_8']].apply(char_min,axis=1)
tele["min_arpu_3g"]= tele[['arpu_3g_6','arpu_3g_7','arpu_3g_8']].apply(char_min,axis=1)
tele["min_arpu_2g"]= tele[['arpu_2g_6','arpu_2g_7','arpu_2g_8']].apply(char_min,axis=1)
tele["min_night_pck_user"]= tele[['night_pck_user_6','night_pck_user_7','night_pck_user_8']].apply(char_min,axis=1)
tele["min_av_rech_amt_data"]= tele[['av_rech_amt_data_6','av_rech_amt_data_7','av_rech_amt_data_8']].apply(char_min,axis=1)
tele["min_monthly_2g"]= tele[['monthly_2g_6','monthly_2g_7','monthly_2g_8']].apply(char_min,axis=1)
tele["min_sachet_2g"]= tele[['sachet_2g_6','sachet_2g_7','sachet_2g_8']].apply(char_min,axis=1)
tele["min_monthly_3g"]= tele[['monthly_3g_6','monthly_3g_7','monthly_3g_8']].apply(char_min,axis=1)
tele["min_sachet_3g"]= tele[['sachet_3g_6','sachet_3g_7','sachet_3g_8']].apply(char_min,axis=1)
tele["min_fb_user"]= tele[['fb_user_6','fb_user_7','fb_user_8']].apply(char_min,axis=1)
tele["min_vbc_3g"]= tele[['jun_vbc_3g','jul_vbc_3g','aug_vbc_3g']].apply(char_min,axis=1)


def char_max(row):
    max_of = np.max(row)
    return max_of


tele["max_arpu"]= tele[['arpu_6','arpu_7','arpu_8']].apply(char_max,axis=1)
tele["max_onnet_mou"]= tele[['onnet_mou_6','onnet_mou_7','onnet_mou_8']].apply(char_max,axis=1)
tele["max_ofnet_mou"]= tele[['offnet_mou_6','offnet_mou_7','offnet_mou_8']].apply(char_max,axis=1)
tele["max_roam_ic_mou"]= tele[['roam_ic_mou_6','roam_ic_mou_7','roam_ic_mou_8']].apply(char_max,axis=1)
tele["max_roam_og_mou"]= tele[['roam_og_mou_6','roam_og_mou_7','roam_og_mou_8']].apply(char_max,axis=1)
tele["max_loc_og_t2t_mou"]= tele[['loc_og_t2t_mou_6','loc_og_t2t_mou_7','loc_og_t2t_mou_8']].apply(char_max,axis=1)
tele["max_loc_og_t2m_mou"]= tele[['loc_og_t2m_mou_6','loc_og_t2m_mou_7','loc_og_t2m_mou_8']].apply(char_max,axis=1)
tele["max_loc_og_t2f_mou"]= tele[['loc_og_t2f_mou_6','loc_og_t2f_mou_7','loc_og_t2f_mou_8']].apply(char_max,axis=1)
tele["max_loc_og_t2c_mou"]= tele[['loc_og_t2c_mou_6','loc_og_t2c_mou_7','loc_og_t2c_mou_8']].apply(char_max,axis=1)
tele["max_loc_og_mou"]= tele[['loc_og_mou_6','loc_og_mou_7','loc_og_mou_8']].apply(char_max,axis=1)
tele["max_std_og_t2t_mou"]= tele[['std_og_t2t_mou_6','std_og_t2t_mou_7','std_og_t2t_mou_8']].apply(char_max,axis=1)
tele["max_std_og_t2m_mou"]= tele[['std_og_t2m_mou_6','std_og_t2m_mou_7','std_og_t2m_mou_8']].apply(char_max,axis=1)
tele["max_std_og_t2f_mou"]= tele[['std_og_t2f_mou_6','std_og_t2f_mou_7','std_og_t2f_mou_8']].apply(char_max,axis=1)
tele["max_std_og_t2c_mou"]= tele[['std_og_t2c_mou_6','std_og_t2c_mou_7','std_og_t2c_mou_8']].apply(char_max,axis=1)
tele["max_isd_og_mou"]= tele[['isd_og_mou_6','isd_og_mou_7','isd_og_mou_8']].apply(char_max,axis=1)
tele["max_std_og_mou"]= tele[['std_og_mou_6','std_og_mou_7','std_og_mou_8']].apply(char_max,axis=1)
tele["max_spl_og_mou"]= tele[['spl_og_mou_6','spl_og_mou_7','spl_og_mou_8']].apply(char_max,axis=1)
tele["max_og_others"]= tele[['og_others_6','og_others_7','og_others_8']].apply(char_max,axis=1)
tele["max_total_og_mou"]= tele[['total_og_mou_6','total_og_mou_7','total_og_mou_8']].apply(char_max,axis=1)
tele["max_loc_ic_t2t_mou"]= tele[['loc_ic_t2t_mou_6','loc_ic_t2t_mou_7','loc_ic_t2t_mou_8']].apply(char_max,axis=1)
tele["max_loc_ic_t2m_mou"]= tele[['loc_ic_t2m_mou_6','loc_ic_t2m_mou_7','loc_ic_t2m_mou_8']].apply(char_max,axis=1)
tele["max_loc_ic_t2f_mou"]= tele[['loc_ic_t2f_mou_6','loc_ic_t2f_mou_7','loc_ic_t2f_mou_8']].apply(char_max,axis=1)
tele["max_loc_ic_mou"]= tele[['loc_ic_mou_6','loc_ic_mou_7','loc_ic_mou_8']].apply(char_max,axis=1)
tele["max_std_ic_t2t_mou"]= tele[['std_ic_t2t_mou_6','std_ic_t2t_mou_7','std_ic_t2t_mou_8']].apply(char_max,axis=1)
tele["max_std_ic_t2m_mou"]= tele[['std_ic_t2m_mou_6','std_ic_t2m_mou_7','std_ic_t2m_mou_8']].apply(char_max,axis=1)
tele["max_std_ic_t2f_mou"]= tele[['std_ic_t2f_mou_6','std_ic_t2f_mou_7','std_ic_t2f_mou_8']].apply(char_max,axis=1)
tele["max_std_ic_t2o_mou"]= tele[['std_ic_t2o_mou_6','std_ic_t2o_mou_7','std_ic_t2o_mou_8']].apply(char_max,axis=1)
tele["max_std_ic_mou"]= tele[['std_ic_mou_6','std_ic_mou_7','std_ic_mou_8']].apply(char_max,axis=1)
tele["max_total_ic_mou"]= tele[['total_ic_mou_6','total_ic_mou_7','total_ic_mou_8']].apply(char_max,axis=1)
tele["max_spl_ic_mou"]= tele[['spl_ic_mou_6','spl_ic_mou_7','spl_ic_mou_8']].apply(char_max,axis=1)
tele["max_isd_ic_mou"]= tele[['isd_ic_mou_6','isd_ic_mou_7','isd_ic_mou_8']].apply(char_max,axis=1)
tele["max_ic_others"]= tele[['ic_others_6','ic_others_7','ic_others_8']].apply(char_max,axis=1)
tele["max_total_rech_num"]= tele[['total_rech_num_6','total_rech_num_7','total_rech_num_8']].apply(char_max,axis=1)
tele["max_total_rech_amt"]= tele[['total_rech_amt_6','total_rech_amt_7','total_rech_amt_8']].apply(char_max,axis=1)
tele["max_max_rech_amt"]= tele[['max_rech_amt_6','max_rech_amt_7','max_rech_amt_8']].apply(char_max,axis=1)
tele["max_last_day_rch_amt"]= tele[['last_day_rch_amt_6','last_day_rch_amt_7','last_day_rch_amt_8']].apply(char_max,axis=1)
tele["max_total_rech_data"]= tele[['total_rech_data_6','total_rech_data_7','total_rech_data_8']].apply(char_max,axis=1)
tele["max_max_rech_data"]= tele[['max_rech_data_6','max_rech_data_7','max_rech_data_8']].apply(char_max,axis=1)
tele["max_count_rech_2g"]= tele[['count_rech_2g_6','count_rech_2g_7','count_rech_2g_8']].apply(char_max,axis=1)
tele["max_count_rech_3g"]= tele[['count_rech_3g_6','count_rech_3g_7','count_rech_3g_8']].apply(char_max,axis=1)
tele["max_av_rech_amt_data"]= tele[['av_rech_amt_data_6','av_rech_amt_data_7','av_rech_amt_data_8']].apply(char_max,axis=1)
tele["max_vol_2g_mb"]= tele[['vol_2g_mb_6','vol_2g_mb_7','vol_2g_mb_8']].apply(char_max,axis=1)
tele["max_vol_3g_mb"]= tele[['vol_3g_mb_6','vol_3g_mb_7','vol_3g_mb_8']].apply(char_max,axis=1)
tele["max_arpu_3g"]= tele[['arpu_3g_6','arpu_3g_7','arpu_3g_8']].apply(char_max,axis=1)
tele["max_arpu_2g"]= tele[['arpu_2g_6','arpu_2g_7','arpu_2g_8']].apply(char_max,axis=1)
tele["max_night_pck_user"]= tele[['night_pck_user_6','night_pck_user_7','night_pck_user_8']].apply(char_max,axis=1)
tele["max_av_rech_amt_data"]= tele[['av_rech_amt_data_6','av_rech_amt_data_7','av_rech_amt_data_8']].apply(char_max,axis=1)
tele["max_monthly_2g"]= tele[['monthly_2g_6','monthly_2g_7','monthly_2g_8']].apply(char_max,axis=1)
tele["max_sachet_2g"]= tele[['sachet_2g_6','sachet_2g_7','sachet_2g_8']].apply(char_max,axis=1)
tele["max_monthly_3g"]= tele[['monthly_3g_6','monthly_3g_7','monthly_3g_8']].apply(char_max,axis=1)
tele["max_sachet_3g"]= tele[['sachet_3g_6','sachet_3g_7','sachet_3g_8']].apply(char_max,axis=1)
tele["max_fb_user"]= tele[['fb_user_6','fb_user_7','fb_user_8']].apply(char_max,axis=1)
tele["max_vbc_3g"]= tele[['jun_vbc_3g','jul_vbc_3g','aug_vbc_3g']].apply(char_max,axis=1)


def char_sum(row):
    sum_of = np.sum(row)
    return sum_of


tele["sum_arpu"]= tele[['arpu_6','arpu_7','arpu_8']].apply(char_sum,axis=1)
tele["sum_onnet_mou"]= tele[['onnet_mou_6','onnet_mou_7','onnet_mou_8']].apply(char_sum,axis=1)
tele["sum_ofnet_mou"]= tele[['offnet_mou_6','offnet_mou_7','offnet_mou_8']].apply(char_sum,axis=1)
tele["sum_roam_ic_mou"]= tele[['roam_ic_mou_6','roam_ic_mou_7','roam_ic_mou_8']].apply(char_sum,axis=1)
tele["sum_roam_og_mou"]= tele[['roam_og_mou_6','roam_og_mou_7','roam_og_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_og_t2t_mou"]= tele[['loc_og_t2t_mou_6','loc_og_t2t_mou_7','loc_og_t2t_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_og_t2m_mou"]= tele[['loc_og_t2m_mou_6','loc_og_t2m_mou_7','loc_og_t2m_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_og_t2f_mou"]= tele[['loc_og_t2f_mou_6','loc_og_t2f_mou_7','loc_og_t2f_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_og_t2c_mou"]= tele[['loc_og_t2c_mou_6','loc_og_t2c_mou_7','loc_og_t2c_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_og_mou"]= tele[['loc_og_mou_6','loc_og_mou_7','loc_og_mou_8']].apply(char_sum,axis=1)
tele["sum_std_og_t2t_mou"]= tele[['std_og_t2t_mou_6','std_og_t2t_mou_7','std_og_t2t_mou_8']].apply(char_sum,axis=1)
tele["sum_std_og_t2m_mou"]= tele[['std_og_t2m_mou_6','std_og_t2m_mou_7','std_og_t2m_mou_8']].apply(char_sum,axis=1)
tele["sum_std_og_t2f_mou"]= tele[['std_og_t2f_mou_6','std_og_t2f_mou_7','std_og_t2f_mou_8']].apply(char_sum,axis=1)
tele["sum_std_og_t2c_mou"]= tele[['std_og_t2c_mou_6','std_og_t2c_mou_7','std_og_t2c_mou_8']].apply(char_sum,axis=1)
tele["sum_isd_og_mou"]= tele[['isd_og_mou_6','isd_og_mou_7','isd_og_mou_8']].apply(char_sum,axis=1)
tele["sum_std_og_mou"]= tele[['std_og_mou_6','std_og_mou_7','std_og_mou_8']].apply(char_sum,axis=1)
tele["sum_spl_og_mou"]= tele[['spl_og_mou_6','spl_og_mou_7','spl_og_mou_8']].apply(char_sum,axis=1)
tele["sum_og_others"]= tele[['og_others_6','og_others_7','og_others_8']].apply(char_sum,axis=1)
tele["sum_total_og_mou"]= tele[['total_og_mou_6','total_og_mou_7','total_og_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_ic_t2t_mou"]= tele[['loc_ic_t2t_mou_6','loc_ic_t2t_mou_7','loc_ic_t2t_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_ic_t2m_mou"]= tele[['loc_ic_t2m_mou_6','loc_ic_t2m_mou_7','loc_ic_t2m_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_ic_t2f_mou"]= tele[['loc_ic_t2f_mou_6','loc_ic_t2f_mou_7','loc_ic_t2f_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_ic_mou"]= tele[['loc_ic_mou_6','loc_ic_mou_7','loc_ic_mou_8']].apply(char_sum,axis=1)
tele["sum_std_ic_t2t_mou"]= tele[['std_ic_t2t_mou_6','std_ic_t2t_mou_7','std_ic_t2t_mou_8']].apply(char_sum,axis=1)
tele["sum_std_ic_t2m_mou"]= tele[['std_ic_t2m_mou_6','std_ic_t2m_mou_7','std_ic_t2m_mou_8']].apply(char_sum,axis=1)
tele["sum_std_ic_t2f_mou"]= tele[['std_ic_t2f_mou_6','std_ic_t2f_mou_7','std_ic_t2f_mou_8']].apply(char_sum,axis=1)
tele["sum_std_ic_t2o_mou"]= tele[['std_ic_t2o_mou_6','std_ic_t2o_mou_7','std_ic_t2o_mou_8']].apply(char_sum,axis=1)
tele["sum_std_ic_mou"]= tele[['std_ic_mou_6','std_ic_mou_7','std_ic_mou_8']].apply(char_sum,axis=1)
tele["sum_total_ic_mou"]= tele[['total_ic_mou_6','total_ic_mou_7','total_ic_mou_8']].apply(char_sum,axis=1)
tele["sum_spl_ic_mou"]= tele[['spl_ic_mou_6','spl_ic_mou_7','spl_ic_mou_8']].apply(char_sum,axis=1)
tele["sum_isd_ic_mou"]= tele[['isd_ic_mou_6','isd_ic_mou_7','isd_ic_mou_8']].apply(char_sum,axis=1)
tele["sum_ic_others"]= tele[['ic_others_6','ic_others_7','ic_others_8']].apply(char_sum,axis=1)
tele["sum_total_rech_num"]= tele[['total_rech_num_6','total_rech_num_7','total_rech_num_8']].apply(char_sum,axis=1)
tele["sum_total_rech_amt"]= tele[['total_rech_amt_6','total_rech_amt_7','total_rech_amt_8']].apply(char_sum,axis=1)
tele["sum_max_rech_amt"]= tele[['max_rech_amt_6','max_rech_amt_7','max_rech_amt_8']].apply(char_sum,axis=1)
tele["sum_last_day_rch_amt"]= tele[['last_day_rch_amt_6','last_day_rch_amt_7','last_day_rch_amt_8']].apply(char_sum,axis=1)
tele["sum_total_rech_data"]= tele[['total_rech_data_6','total_rech_data_7','total_rech_data_8']].apply(char_sum,axis=1)
tele["sum_max_rech_data"]= tele[['max_rech_data_6','max_rech_data_7','max_rech_data_8']].apply(char_sum,axis=1)
tele["sum_count_rech_2g"]= tele[['count_rech_2g_6','count_rech_2g_7','count_rech_2g_8']].apply(char_sum,axis=1)
tele["sum_count_rech_3g"]= tele[['count_rech_3g_6','count_rech_3g_7','count_rech_3g_8']].apply(char_sum,axis=1)
tele["sum_av_rech_amt_data"]= tele[['av_rech_amt_data_6','av_rech_amt_data_7','av_rech_amt_data_8']].apply(char_sum,axis=1)
tele["sum_vol_2g_mb"]= tele[['vol_2g_mb_6','vol_2g_mb_7','vol_2g_mb_8']].apply(char_sum,axis=1)
tele["sum_vol_3g_mb"]= tele[['vol_3g_mb_6','vol_3g_mb_7','vol_3g_mb_8']].apply(char_sum,axis=1)
tele["sum_arpu_3g"]= tele[['arpu_3g_6','arpu_3g_7','arpu_3g_8']].apply(char_sum,axis=1)
tele["sum_arpu_2g"]= tele[['arpu_2g_6','arpu_2g_7','arpu_2g_8']].apply(char_sum,axis=1)
tele["sum_night_pck_user"]= tele[['night_pck_user_6','night_pck_user_7','night_pck_user_8']].apply(char_sum,axis=1)
tele["sum_av_rech_amt_data"]= tele[['av_rech_amt_data_6','av_rech_amt_data_7','av_rech_amt_data_8']].apply(char_sum,axis=1)
tele["sum_monthly_2g"]= tele[['monthly_2g_6','monthly_2g_7','monthly_2g_8']].apply(char_sum,axis=1)
tele["sum_sachet_2g"]= tele[['sachet_2g_6','sachet_2g_7','sachet_2g_8']].apply(char_sum,axis=1)
tele["sum_monthly_3g"]= tele[['monthly_3g_6','monthly_3g_7','monthly_3g_8']].apply(char_sum,axis=1)
tele["sum_sachet_3g"]= tele[['sachet_3g_6','sachet_3g_7','sachet_3g_8']].apply(char_sum,axis=1)
tele["sum_fb_user"]= tele[['fb_user_6','fb_user_7','fb_user_8']].apply(char_sum,axis=1)
tele["sum_vbc_3g"]= tele[['jun_vbc_3g','jul_vbc_3g','aug_vbc_3g']].apply(char_sum,axis=1)


def char_mean(row):
    mean_of = np.mean(row)
    return mean_of


tele["mean_arpu"]= tele[['arpu_6','arpu_7','arpu_8']].apply(char_mean,axis=1)
tele["mean_onnet_mou"]= tele[['onnet_mou_6','onnet_mou_7','onnet_mou_8']].apply(char_mean,axis=1)
tele["mean_ofnet_mou"]= tele[['offnet_mou_6','offnet_mou_7','offnet_mou_8']].apply(char_mean,axis=1)
tele["mean_roam_ic_mou"]= tele[['roam_ic_mou_6','roam_ic_mou_7','roam_ic_mou_8']].apply(char_mean,axis=1)
tele["mean_roam_og_mou"]= tele[['roam_og_mou_6','roam_og_mou_7','roam_og_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_og_t2t_mou"]= tele[['loc_og_t2t_mou_6','loc_og_t2t_mou_7','loc_og_t2t_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_og_t2m_mou"]= tele[['loc_og_t2m_mou_6','loc_og_t2m_mou_7','loc_og_t2m_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_og_t2f_mou"]= tele[['loc_og_t2f_mou_6','loc_og_t2f_mou_7','loc_og_t2f_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_og_t2c_mou"]= tele[['loc_og_t2c_mou_6','loc_og_t2c_mou_7','loc_og_t2c_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_og_mou"]= tele[['loc_og_mou_6','loc_og_mou_7','loc_og_mou_8']].apply(char_mean,axis=1)
tele["mean_std_og_t2t_mou"]= tele[['std_og_t2t_mou_6','std_og_t2t_mou_7','std_og_t2t_mou_8']].apply(char_mean,axis=1)
tele["mean_std_og_t2m_mou"]= tele[['std_og_t2m_mou_6','std_og_t2m_mou_7','std_og_t2m_mou_8']].apply(char_mean,axis=1)
tele["mean_std_og_t2f_mou"]= tele[['std_og_t2f_mou_6','std_og_t2f_mou_7','std_og_t2f_mou_8']].apply(char_mean,axis=1)
tele["mean_std_og_t2c_mou"]= tele[['std_og_t2c_mou_6','std_og_t2c_mou_7','std_og_t2c_mou_8']].apply(char_mean,axis=1)
tele["mean_isd_og_mou"]= tele[['isd_og_mou_6','isd_og_mou_7','isd_og_mou_8']].apply(char_mean,axis=1)
tele["mean_std_og_mou"]= tele[['std_og_mou_6','std_og_mou_7','std_og_mou_8']].apply(char_mean,axis=1)
tele["mean_spl_og_mou"]= tele[['spl_og_mou_6','spl_og_mou_7','spl_og_mou_8']].apply(char_mean,axis=1)
tele["mean_og_others"]= tele[['og_others_6','og_others_7','og_others_8']].apply(char_mean,axis=1)
tele["mean_total_og_mou"]= tele[['total_og_mou_6','total_og_mou_7','total_og_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_ic_t2t_mou"]= tele[['loc_ic_t2t_mou_6','loc_ic_t2t_mou_7','loc_ic_t2t_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_ic_t2m_mou"]= tele[['loc_ic_t2m_mou_6','loc_ic_t2m_mou_7','loc_ic_t2m_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_ic_t2f_mou"]= tele[['loc_ic_t2f_mou_6','loc_ic_t2f_mou_7','loc_ic_t2f_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_ic_mou"]= tele[['loc_ic_mou_6','loc_ic_mou_7','loc_ic_mou_8']].apply(char_mean,axis=1)
tele["mean_std_ic_t2t_mou"]= tele[['std_ic_t2t_mou_6','std_ic_t2t_mou_7','std_ic_t2t_mou_8']].apply(char_mean,axis=1)
tele["mean_std_ic_t2m_mou"]= tele[['std_ic_t2m_mou_6','std_ic_t2m_mou_7','std_ic_t2m_mou_8']].apply(char_mean,axis=1)
tele["mean_std_ic_t2f_mou"]= tele[['std_ic_t2f_mou_6','std_ic_t2f_mou_7','std_ic_t2f_mou_8']].apply(char_mean,axis=1)
tele["mean_std_ic_t2o_mou"]= tele[['std_ic_t2o_mou_6','std_ic_t2o_mou_7','std_ic_t2o_mou_8']].apply(char_mean,axis=1)
tele["mean_std_ic_mou"]= tele[['std_ic_mou_6','std_ic_mou_7','std_ic_mou_8']].apply(char_mean,axis=1)
tele["mean_total_ic_mou"]= tele[['total_ic_mou_6','total_ic_mou_7','total_ic_mou_8']].apply(char_mean,axis=1)
tele["mean_spl_ic_mou"]= tele[['spl_ic_mou_6','spl_ic_mou_7','spl_ic_mou_8']].apply(char_mean,axis=1)
tele["mean_isd_ic_mou"]= tele[['isd_ic_mou_6','isd_ic_mou_7','isd_ic_mou_8']].apply(char_mean,axis=1)
tele["mean_ic_others"]= tele[['ic_others_6','ic_others_7','ic_others_8']].apply(char_mean,axis=1)
tele["mean_total_rech_num"]= tele[['total_rech_num_6','total_rech_num_7','total_rech_num_8']].apply(char_mean,axis=1)
tele["mean_total_rech_amt"]= tele[['total_rech_amt_6','total_rech_amt_7','total_rech_amt_8']].apply(char_mean,axis=1)
tele["mean_max_rech_amt"]= tele[['max_rech_amt_6','max_rech_amt_7','max_rech_amt_8']].apply(char_mean,axis=1)
tele["mean_last_day_rch_amt"]= tele[['last_day_rch_amt_6','last_day_rch_amt_7','last_day_rch_amt_8']].apply(char_mean,axis=1)
tele["mean_total_rech_data"]= tele[['total_rech_data_6','total_rech_data_7','total_rech_data_8']].apply(char_mean,axis=1)
tele["mean_max_rech_data"]= tele[['max_rech_data_6','max_rech_data_7','max_rech_data_8']].apply(char_mean,axis=1)
tele["mean_count_rech_2g"]= tele[['count_rech_2g_6','count_rech_2g_7','count_rech_2g_8']].apply(char_mean,axis=1)
tele["mean_count_rech_3g"]= tele[['count_rech_3g_6','count_rech_3g_7','count_rech_3g_8']].apply(char_mean,axis=1)
tele["mean_av_rech_amt_data"]= tele[['av_rech_amt_data_6','av_rech_amt_data_7','av_rech_amt_data_8']].apply(char_mean,axis=1)
tele["mean_vol_2g_mb"]= tele[['vol_2g_mb_6','vol_2g_mb_7','vol_2g_mb_8']].apply(char_mean,axis=1)
tele["mean_vol_3g_mb"]= tele[['vol_3g_mb_6','vol_3g_mb_7','vol_3g_mb_8']].apply(char_mean,axis=1)
tele["mean_arpu_3g"]= tele[['arpu_3g_6','arpu_3g_7','arpu_3g_8']].apply(char_mean,axis=1)
tele["mean_arpu_2g"]= tele[['arpu_2g_6','arpu_2g_7','arpu_2g_8']].apply(char_mean,axis=1)
tele["mean_night_pck_user"]= tele[['night_pck_user_6','night_pck_user_7','night_pck_user_8']].apply(char_mean,axis=1)
tele["mean_av_rech_amt_data"]= tele[['av_rech_amt_data_6','av_rech_amt_data_7','av_rech_amt_data_8']].apply(char_mean,axis=1)
tele["mean_monthly_2g"]= tele[['monthly_2g_6','monthly_2g_7','monthly_2g_8']].apply(char_mean,axis=1)
tele["mean_sachet_2g"]= tele[['sachet_2g_6','sachet_2g_7','sachet_2g_8']].apply(char_mean,axis=1)
tele["mean_monthly_3g"]= tele[['monthly_3g_6','monthly_3g_7','monthly_3g_8']].apply(char_mean,axis=1)
tele["mean_sachet_3g"]= tele[['sachet_3g_6','sachet_3g_7','sachet_3g_8']].apply(char_mean,axis=1)
tele["mean_fb_user"]= tele[['fb_user_6','fb_user_7','fb_user_8']].apply(char_mean,axis=1)
tele["mean_vbc_3g"]= tele[['jun_vbc_3g','jul_vbc_3g','aug_vbc_3g']].apply(char_mean,axis=1)


def char_mom(x):
    MOM = x[0]/x[1]
    return MOM


tele["mom_arpu"]= tele[['arpu_8','arpu_7']].apply(char_mom,axis=1)
tele["mom_onnet_mou"]= tele[['onnet_mou_8','onnet_mou_7']].apply(char_mom,axis=1)
tele["mom_ofnet_mou"]= tele[['offnet_mou_8','offnet_mou_7']].apply(char_mom,axis=1)
tele["mom_roam_ic_mou"]= tele[['roam_ic_mou_8','roam_ic_mou_7']].apply(char_mom,axis=1)
tele["mom_roam_og_mou"]= tele[['roam_og_mou_8','roam_og_mou_7']].apply(char_mom,axis=1)
tele["mom_loc_og_t2t_mou"]= tele[['loc_og_t2t_mou_8','loc_og_t2t_mou_7']].apply(char_mom,axis=1)
tele["mom_loc_og_t2m_mou"]= tele[['loc_og_t2m_mou_8','loc_og_t2m_mou_7']].apply(char_mom,axis=1)
tele["mom_loc_og_t2f_mou"]= tele[['loc_og_t2f_mou_8','loc_og_t2f_mou_7']].apply(char_mom,axis=1)
tele["mom_loc_og_t2c_mou"]= tele[['loc_og_t2c_mou_8','loc_og_t2c_mou_7']].apply(char_mom,axis=1)
tele["mom_loc_og_mou"]= tele[['loc_og_mou_8','loc_og_mou_7']].apply(char_mom,axis=1)
tele["mom_std_og_t2t_mou"]= tele[['std_og_t2t_mou_8','std_og_t2t_mou_7']].apply(char_mom,axis=1)
tele["mom_std_og_t2m_mou"]= tele[['std_og_t2m_mou_8','std_og_t2m_mou_7']].apply(char_mom,axis=1)
tele["mom_std_og_t2f_mou"]= tele[['std_og_t2f_mou_8','std_og_t2f_mou_7']].apply(char_mom,axis=1)
tele["mom_std_og_t2c_mou"]= tele[['std_og_t2c_mou_8','std_og_t2c_mou_7']].apply(char_mom,axis=1)
tele["mom_isd_og_mou"]= tele[['isd_og_mou_8','isd_og_mou_7']].apply(char_mom,axis=1)
tele["mom_std_og_mou"]= tele[['std_og_mou_8','std_og_mou_7']].apply(char_mom,axis=1)
tele["mom_spl_og_mou"]= tele[['spl_og_mou_8','spl_og_mou_7']].apply(char_mom,axis=1)
tele["mom_og_others"]= tele[['og_others_8','og_others_7']].apply(char_mom,axis=1)
tele["mom_total_og_mou"]= tele[['total_og_mou_8','total_og_mou_7']].apply(char_mom,axis=1)
tele["mom_loc_ic_t2t_mou"]= tele[['loc_ic_t2t_mou_8','loc_ic_t2t_mou_7']].apply(char_mom,axis=1)
tele["mom_loc_ic_t2m_mou"]= tele[['loc_ic_t2m_mou_8','loc_ic_t2m_mou_7']].apply(char_mom,axis=1)
tele["mom_loc_ic_t2f_mou"]= tele[['loc_ic_t2f_mou_8','loc_ic_t2f_mou_7']].apply(char_mom,axis=1)
tele["mom_loc_ic_mou"]= tele[['loc_ic_mou_8','loc_ic_mou_7']].apply(char_mom,axis=1)
tele["mom_std_ic_t2t_mou"]= tele[['std_ic_t2t_mou_8','std_ic_t2t_mou_7']].apply(char_mom,axis=1)
tele["mom_std_ic_t2m_mou"]= tele[['std_ic_t2m_mou_8','std_ic_t2m_mou_7']].apply(char_mom,axis=1)
tele["mom_std_ic_t2f_mou"]= tele[['std_ic_t2f_mou_8','std_ic_t2f_mou_7']].apply(char_mom,axis=1)
tele["mom_std_ic_t2o_mou"]= tele[['std_ic_t2o_mou_8','std_ic_t2o_mou_7']].apply(char_mom,axis=1)
tele["mom_std_ic_mou"]= tele[['std_ic_mou_8','std_ic_mou_7']].apply(char_mom,axis=1)
tele["mom_total_ic_mou"]= tele[['total_ic_mou_8','total_ic_mou_7']].apply(char_mom,axis=1)
tele["mom_spl_ic_mou"]= tele[['spl_ic_mou_8','spl_ic_mou_7']].apply(char_mom,axis=1)
tele["mom_isd_ic_mou"]= tele[['isd_ic_mou_8','isd_ic_mou_7']].apply(char_mom,axis=1)
tele["mom_ic_others"]= tele[['ic_others_8','ic_others_7']].apply(char_mom,axis=1)
tele["mom_total_rech_num"]= tele[['total_rech_num_8','total_rech_num_7']].apply(char_mom,axis=1)
tele["mom_total_rech_amt"]= tele[['total_rech_amt_8','total_rech_amt_7']].apply(char_mom,axis=1)
tele["mom_max_rech_amt"]= tele[['max_rech_amt_8','max_rech_amt_7']].apply(char_mom,axis=1)
tele["mom_last_day_rch_amt"]= tele[['last_day_rch_amt_8','last_day_rch_amt_7']].apply(char_mom,axis=1)
tele["mom_total_rech_data"]= tele[['total_rech_data_8','total_rech_data_7']].apply(char_mom,axis=1)
tele["mom_max_rech_data"]= tele[['max_rech_data_8','max_rech_data_7']].apply(char_mom,axis=1)
tele["mom_count_rech_2g"]= tele[['count_rech_2g_8','count_rech_2g_7']].apply(char_mom,axis=1)
tele["mom_count_rech_3g"]= tele[['count_rech_3g_8','count_rech_3g_7']].apply(char_mom,axis=1)
tele["mom_av_rech_amt_data"]= tele[['av_rech_amt_data_8','av_rech_amt_data_7']].apply(char_mom,axis=1)
tele["mom_vol_2g_mb"]= tele[['vol_2g_mb_8','vol_2g_mb_7']].apply(char_mom,axis=1)
tele["mom_vol_3g_mb"]= tele[['vol_3g_mb_8','vol_3g_mb_7']].apply(char_mom,axis=1)
tele["mom_arpu_3g"]= tele[['arpu_3g_8','arpu_3g_7']].apply(char_mom,axis=1)
tele["mom_arpu_2g"]= tele[['arpu_2g_8','arpu_2g_7']].apply(char_mom,axis=1)
tele["mom_night_pck_user"]= tele[['night_pck_user_8','night_pck_user_7']].apply(char_mom,axis=1)
tele["mom_av_rech_amt_data"]= tele[['av_rech_amt_data_8','av_rech_amt_data_7']].apply(char_mom,axis=1)
tele["mom_monthly_2g"]= tele[['monthly_2g_8','monthly_2g_7']].apply(char_mom,axis=1)
tele["mom_sachet_2g"]= tele[['sachet_2g_8','sachet_2g_7']].apply(char_mom,axis=1)
tele["mom_monthly_3g"]= tele[['monthly_3g_8','monthly_3g_7']].apply(char_mom,axis=1)
tele["mom_sachet_3g"]= tele[['sachet_3g_8','sachet_3g_7']].apply(char_mom,axis=1)
tele["mom_fb_user"]= tele[['fb_user_8','fb_user_7']].apply(char_mom,axis=1)
tele["mom_vbc_3g"]= tele[['aug_vbc_3g','jul_vbc_3g']].apply(char_mom,axis=1)

<ipython-input-14-5bbe2a8d61e6>:2: RuntimeWarning: divide by zero encountered in double_scalars
  MOM = x[0]/x[1]
<ipython-input-14-5bbe2a8d61e6>:2: RuntimeWarning: invalid value encountered in double_scalars
  MOM = x[0]/x[1]
<ipython-input-14-5bbe2a8d61e6>:2: RuntimeWarning: divide by zero encountered in longlong_scalars
  MOM = x[0]/x[1]
<ipython-input-14-5bbe2a8d61e6>:2: RuntimeWarning: invalid value encountered in longlong_scalars
  MOM = x[0]/x[1]


def char_moq(x):
    MOQ = x[0]/x[0]+x[1]+x[2]/3
    return MOQ


tele["moq_arpu"]= tele[['arpu_8','arpu_7','arpu_6']].apply(char_moq,axis=1)
tele["moq_onnet_mou"]= tele[['onnet_mou_8','onnet_mou_7','onnet_mou_6']].apply(char_moq,axis=1)
tele["moq_ofnet_mou"]= tele[['offnet_mou_8','offnet_mou_7','offnet_mou_6']].apply(char_moq,axis=1)
tele["moq_roam_ic_mou"]= tele[['roam_ic_mou_8','roam_ic_mou_7','roam_ic_mou_6']].apply(char_moq,axis=1)
tele["moq_roam_og_mou"]= tele[['roam_og_mou_8','roam_og_mou_7','roam_og_mou_6']].apply(char_moq,axis=1)
tele["moq_loc_og_t2t_mou"]= tele[['loc_og_t2t_mou_8','loc_og_t2t_mou_7','loc_og_t2t_mou_6']].apply(char_moq,axis=1)
tele["moq_loc_og_t2m_mou"]= tele[['loc_og_t2m_mou_8','loc_og_t2m_mou_7','loc_og_t2m_mou_6']].apply(char_moq,axis=1)
tele["moq_loc_og_t2f_mou"]= tele[['loc_og_t2f_mou_8','loc_og_t2f_mou_7','loc_og_t2f_mou_6']].apply(char_moq,axis=1)
tele["moq_loc_og_t2c_mou"]= tele[['loc_og_t2c_mou_8','loc_og_t2c_mou_7','loc_og_t2c_mou_6']].apply(char_moq,axis=1)
tele["moq_loc_og_mou"]= tele[['loc_og_mou_8','loc_og_mou_7','loc_og_mou_6']].apply(char_moq,axis=1)
tele["moq_std_og_t2t_mou"]= tele[['std_og_t2t_mou_8','std_og_t2t_mou_7','std_og_t2t_mou_6']].apply(char_moq,axis=1)
tele["moq_std_og_t2m_mou"]= tele[['std_og_t2m_mou_8','std_og_t2m_mou_7','std_og_t2m_mou_6']].apply(char_moq,axis=1)
tele["moq_std_og_t2f_mou"]= tele[['std_og_t2f_mou_8','std_og_t2f_mou_7','std_og_t2f_mou_6']].apply(char_moq,axis=1)
tele["moq_std_og_t2c_mou"]= tele[['std_og_t2c_mou_8','std_og_t2c_mou_7','std_og_t2c_mou_6']].apply(char_moq,axis=1)
tele["moq_isd_og_mou"]= tele[['isd_og_mou_8','isd_og_mou_7','isd_og_mou_6']].apply(char_moq,axis=1)
tele["moq_std_og_mou"]= tele[['std_og_mou_8','std_og_mou_7','std_og_mou_6']].apply(char_moq,axis=1)
tele["moq_spl_og_mou"]= tele[['spl_og_mou_8','spl_og_mou_7','spl_og_mou_6']].apply(char_moq,axis=1)
tele["moq_og_others"]= tele[['og_others_8','og_others_7','og_others_6']].apply(char_moq,axis=1)
tele["moq_total_og_mou"]= tele[['total_og_mou_8','total_og_mou_7','total_og_mou_6']].apply(char_moq,axis=1)
tele["moq_loc_ic_t2t_mou"]= tele[['loc_ic_t2t_mou_8','loc_ic_t2t_mou_7','loc_ic_t2t_mou_6']].apply(char_moq,axis=1)
tele["moq_loc_ic_t2m_mou"]= tele[['loc_ic_t2m_mou_8','loc_ic_t2m_mou_7','loc_ic_t2m_mou_6']].apply(char_moq,axis=1)
tele["moq_loc_ic_t2f_mou"]= tele[['loc_ic_t2f_mou_8','loc_ic_t2f_mou_7','loc_ic_t2f_mou_6']].apply(char_moq,axis=1)
tele["moq_loc_ic_mou"]= tele[['loc_ic_mou_8','loc_ic_mou_7','loc_ic_mou_6']].apply(char_moq,axis=1)
tele["moq_std_ic_t2t_mou"]= tele[['std_ic_t2t_mou_8','std_ic_t2t_mou_7','std_ic_t2t_mou_6']].apply(char_moq,axis=1)
tele["moq_std_ic_t2m_mou"]= tele[['std_ic_t2m_mou_8','std_ic_t2m_mou_7','std_ic_t2m_mou_6']].apply(char_moq,axis=1)
tele["moq_std_ic_t2f_mou"]= tele[['std_ic_t2f_mou_8','std_ic_t2f_mou_7','std_ic_t2f_mou_6']].apply(char_moq,axis=1)
tele["moq_std_ic_t2o_mou"]= tele[['std_ic_t2o_mou_8','std_ic_t2o_mou_7','std_ic_t2o_mou_6']].apply(char_moq,axis=1)
tele["moq_std_ic_mou"]= tele[['std_ic_mou_8','std_ic_mou_7','std_ic_mou_6']].apply(char_moq,axis=1)
tele["moq_total_ic_mou"]= tele[['total_ic_mou_8','total_ic_mou_7','total_ic_mou_6']].apply(char_moq,axis=1)
tele["moq_spl_ic_mou"]= tele[['spl_ic_mou_8','spl_ic_mou_7','spl_ic_mou_6']].apply(char_moq,axis=1)
tele["moq_isd_ic_mou"]= tele[['isd_ic_mou_8','isd_ic_mou_7','isd_ic_mou_6']].apply(char_moq,axis=1)
tele["moq_ic_others"]= tele[['ic_others_8','ic_others_7','ic_others_6']].apply(char_moq,axis=1)
tele["moq_total_rech_num"]= tele[['total_rech_num_8','total_rech_num_7','total_rech_num_6']].apply(char_moq,axis=1)
tele["moq_total_rech_amt"]= tele[['total_rech_amt_8','total_rech_amt_7','total_rech_amt_6']].apply(char_moq,axis=1)
tele["moq_max_rech_amt"]= tele[['max_rech_amt_8','max_rech_amt_7','max_rech_amt_6']].apply(char_moq,axis=1)
tele["moq_last_day_rch_amt"]= tele[['last_day_rch_amt_8','last_day_rch_amt_7','last_day_rch_amt_6']].apply(char_moq,axis=1)
tele["moq_total_rech_data"]= tele[['total_rech_data_8','total_rech_data_7','total_rech_data_6']].apply(char_moq,axis=1)
tele["moq_max_rech_data"]= tele[['max_rech_data_8','max_rech_data_7','max_rech_data_6']].apply(char_moq,axis=1)
tele["moq_count_rech_2g"]= tele[['count_rech_2g_8','count_rech_2g_7','count_rech_2g_6']].apply(char_moq,axis=1)
tele["moq_count_rech_3g"]= tele[['count_rech_3g_8','count_rech_3g_7','count_rech_3g_6']].apply(char_moq,axis=1)
tele["moq_av_rech_amt_data"]= tele[['av_rech_amt_data_8','av_rech_amt_data_7','av_rech_amt_data_6']].apply(char_moq,axis=1)
tele["moq_vol_2g_mb"]= tele[['vol_2g_mb_8','vol_2g_mb_7','vol_2g_mb_6']].apply(char_moq,axis=1)
tele["moq_vol_3g_mb"]= tele[['vol_3g_mb_8','vol_3g_mb_7','vol_3g_mb_6']].apply(char_moq,axis=1)
tele["moq_arpu_3g"]= tele[['arpu_3g_8','arpu_3g_7','arpu_3g_6']].apply(char_moq,axis=1)
tele["moq_arpu_2g"]= tele[['arpu_2g_8','arpu_2g_7','arpu_2g_6']].apply(char_moq,axis=1)
tele["moq_night_pck_user"]= tele[['night_pck_user_8','night_pck_user_7','night_pck_user_6']].apply(char_moq,axis=1)
tele["moq_av_rech_amt_data"]= tele[['av_rech_amt_data_8','av_rech_amt_data_7','av_rech_amt_data_6']].apply(char_moq,axis=1)
tele["moq_monthly_2g"]= tele[['monthly_2g_8','monthly_2g_7','monthly_2g_6']].apply(char_moq,axis=1)
tele["moq_sachet_2g"]= tele[['sachet_2g_8','sachet_2g_7','sachet_2g_6']].apply(char_moq,axis=1)
tele["moq_monthly_3g"]= tele[['monthly_3g_8','monthly_3g_7','monthly_3g_6']].apply(char_moq,axis=1)
tele["moq_sachet_3g"]= tele[['sachet_3g_8','sachet_3g_7','sachet_3g_6']].apply(char_moq,axis=1)
tele["moq_vbc_3g"]= tele[['aug_vbc_3g','jul_vbc_3g','jun_vbc_3g']].apply(char_moq,axis=1)

<ipython-input-16-1d2ffc61d510>:2: RuntimeWarning: invalid value encountered in double_scalars
  MOQ = x[0]/x[0]+x[1]+x[2]/3
<ipython-input-16-1d2ffc61d510>:2: RuntimeWarning: invalid value encountered in longlong_scalars
  MOQ = x[0]/x[0]+x[1]+x[2]/3


tele.head()


tele.shape

(99999, 538)


tele["arpu_inlast"]= tele['arpu_8']
tele["onnet_mou_inlast"]= tele['onnet_mou_8']
tele["ofnet_mou_inlast"]= tele['offnet_mou_8']
tele["roam_ic_mou_inlast"]= tele['roam_ic_mou_8']
tele["roam_og_mou_inlast"]= tele['roam_og_mou_8']
tele["loc_og_t2t_mou_inlast"]= tele['loc_og_t2t_mou_8']
tele["loc_og_t2m_mou_inlast"]= tele['loc_og_t2m_mou_8']
tele["loc_og_t2f_mou_inlast"]= tele['loc_og_t2f_mou_8']
tele["loc_og_t2c_mou_inlast"]= tele['loc_og_t2c_mou_8']
tele["loc_og_mou_inlast"]= tele['loc_og_mou_8']
tele["std_og_t2t_mou_inlast"]= tele['std_og_t2t_mou_8']
tele["std_og_t2m_mou_inlast"]= tele['std_og_t2m_mou_8']
tele["std_og_t2f_mou_inlast"]= tele['std_og_t2f_mou_8']
tele["std_og_t2c_mou_inlast"]= tele['std_og_t2c_mou_8']
tele["std_og_mou_inlast"]= tele['std_og_mou_8']
tele["isd_og_mou_inlast"]= tele['isd_og_mou_8']
tele["spl_og_mou_inlast"]= tele['spl_og_mou_8']
tele["og_others_inlast"]= tele['og_others_8']
tele["total_og_mou_inlast"]= tele['total_og_mou_8']
tele["loc_ic_t2t_mou_inlast"]= tele['loc_ic_t2t_mou_8']
tele["loc_ic_t2m_mou_inlast"]= tele['loc_ic_t2m_mou_8']
tele["loc_ic_t2f_mou_inlast"]= tele['loc_ic_t2f_mou_8']
tele["loc_ic_mou_inlast"]= tele['loc_ic_mou_8']
tele["std_ic_t2t_mou_inlast"]= tele['std_ic_t2t_mou_8']
tele["std_ic_t2m_mou_inlast"]= tele['std_ic_t2m_mou_8']
tele["std_ic_t2f_mou_inlast"]= tele['std_ic_t2f_mou_8']
tele["std_ic_t2o_mou_inlast"]= tele['std_ic_t2o_mou_8']
tele["std_ic_mou_inlast"]= tele['std_ic_mou_8']
tele["total_ic_mou_inlast"]= tele['total_ic_mou_8']
tele["spl_ic_mou_inlast"]= tele['spl_ic_mou_8']
tele["isd_ic_mou_inlast"]= tele['isd_ic_mou_8']
tele["ic_others_inlast"]= tele['ic_others_8']
tele["total_rech_num_inlast"]= tele['total_rech_num_8']
tele["total_rech_amt_inlast"]= tele['total_rech_amt_8']
tele["max_rech_amt_inlast"]= tele['max_rech_amt_8']
tele["last_day_rch_amt_inlast"]= tele['last_day_rch_amt_8']
tele["total_rech_data_inlast"]= tele['total_rech_data_8']
tele["max_rech_data_inlast"]= tele['max_rech_data_8']
tele["count_rech_2g_inlast"]= tele['count_rech_2g_8']
tele["count_rech_3g_inlast"]= tele['count_rech_3g_8']
tele["av_rech_amt_data_inlast"]= tele['av_rech_amt_data_8']
tele["vol_2g_mb_inlast"]= tele['vol_2g_mb_8']
tele["vol_3g_mb_inlast"]= tele['vol_3g_mb_8']
tele["arpu_3g_inlast"]= tele['arpu_3g_8']
tele["arpu_2g_inlast"]= tele['arpu_2g_8']
tele["night_pck_user_inlast"]= tele['night_pck_user_8']
tele["monthly_2g_inlast"]= tele['monthly_2g_8']
tele["sachet_2g_inlast"]= tele['sachet_2g_8']
tele["monthly_3g_inlast"]= tele['monthly_3g_8']
tele["sachet_3g_inlast"]= tele['sachet_3g_8']
tele["fb_user_inlast"]= tele['fb_user_8']
tele["aug_vbc_3g_inlast"]= tele['aug_vbc_3g']


## creating target variable 
def taeget(x):
    total_usage = x[0]+x[1]+x[2]+x[3]
    if total_usage==0:
        return '1'
    else:
        return '0'
    return total_usage


tele["churn_flag"]=tele[["total_ic_mou_9","vol_3g_mb_9","vol_2g_mb_9","total_og_mou_9"]].apply(taeget,axis=1)


tele["churn_flag"].value_counts()

0    89808
1    10191
Name: churn_flag, dtype: int64


tele.shape

(99999, 591)


pd.set_option('display.max_columns',1000 )


## droping raw data columns
tele1 =tele.drop(columns=['circle_id',
 'loc_og_t2o_mou',
 'std_og_t2o_mou',
 'loc_ic_t2o_mou','last_date_of_month_6',
 'last_date_of_month_7',
 'last_date_of_month_8',
 'last_date_of_month_9',
 'arpu_6',
 'arpu_7',
 'arpu_8',
 'arpu_9',
 'onnet_mou_6',
 'onnet_mou_7',
 'onnet_mou_8',
 'onnet_mou_9',
 'offnet_mou_6',
 'offnet_mou_7',
 'offnet_mou_8',
 'offnet_mou_9',
 'roam_ic_mou_6',
 'roam_ic_mou_7',
 'roam_ic_mou_8',
 'roam_ic_mou_9',
 'roam_og_mou_6',
 'roam_og_mou_7',
 'roam_og_mou_8',
 'roam_og_mou_9',
 'loc_og_t2t_mou_6',
 'loc_og_t2t_mou_7',
 'loc_og_t2t_mou_8',
 'loc_og_t2t_mou_9',
 'loc_og_t2m_mou_6',
 'loc_og_t2m_mou_7',
 'loc_og_t2m_mou_8',
 'loc_og_t2m_mou_9',
 'loc_og_t2f_mou_6',
 'loc_og_t2f_mou_7',
 'loc_og_t2f_mou_8',
 'loc_og_t2f_mou_9',
 'loc_og_t2c_mou_6',
 'loc_og_t2c_mou_7',
 'loc_og_t2c_mou_8',
 'loc_og_t2c_mou_9',
 'loc_og_mou_6',
 'loc_og_mou_7',
 'loc_og_mou_8',
 'loc_og_mou_9',
 'std_og_t2t_mou_6',
 'std_og_t2t_mou_7',
 'std_og_t2t_mou_8',
 'std_og_t2t_mou_9',
 'std_og_t2m_mou_6',
 'std_og_t2m_mou_7',
 'std_og_t2m_mou_8',
 'std_og_t2m_mou_9',
 'std_og_t2f_mou_6',
 'std_og_t2f_mou_7',
 'std_og_t2f_mou_8',
 'std_og_t2f_mou_9',
 'std_og_t2c_mou_6',
 'std_og_t2c_mou_7',
 'std_og_t2c_mou_8',
 'std_og_t2c_mou_9',
 'std_og_mou_6',
 'std_og_mou_7',
 'std_og_mou_8',
 'std_og_mou_9',
 'isd_og_mou_6',
 'isd_og_mou_7',
 'isd_og_mou_8',
 'isd_og_mou_9',
 'spl_og_mou_6',
 'spl_og_mou_7',
 'spl_og_mou_8',
 'spl_og_mou_9',
 'og_others_6',
 'og_others_7',
 'og_others_8',
 'og_others_9',
 'total_og_mou_6',
 'total_og_mou_7',
 'total_og_mou_8',
 'total_og_mou_9',
 'loc_ic_t2t_mou_6',
 'loc_ic_t2t_mou_7',
 'loc_ic_t2t_mou_8',
 'loc_ic_t2t_mou_9',
 'loc_ic_t2m_mou_6',
 'loc_ic_t2m_mou_7',
 'loc_ic_t2m_mou_8',
 'loc_ic_t2m_mou_9',
 'loc_ic_t2f_mou_6',
 'loc_ic_t2f_mou_7',
 'loc_ic_t2f_mou_8',
 'loc_ic_t2f_mou_9',
 'loc_ic_mou_6',
 'loc_ic_mou_7',
 'loc_ic_mou_8',
 'loc_ic_mou_9',
 'std_ic_t2t_mou_6',
 'std_ic_t2t_mou_7',
 'std_ic_t2t_mou_8',
 'std_ic_t2t_mou_9',
 'std_ic_t2m_mou_6',
 'std_ic_t2m_mou_7',
 'std_ic_t2m_mou_8',
 'std_ic_t2m_mou_9',
 'std_ic_t2f_mou_6',
 'std_ic_t2f_mou_7',
 'std_ic_t2f_mou_8',
 'std_ic_t2f_mou_9',
 'std_ic_t2o_mou_6',
 'std_ic_t2o_mou_7',
 'std_ic_t2o_mou_8',
 'std_ic_t2o_mou_9',
 'std_ic_mou_6',
 'std_ic_mou_7',
 'std_ic_mou_8',
 'std_ic_mou_9',
 'total_ic_mou_6',
 'total_ic_mou_7',
 'total_ic_mou_8',
 'total_ic_mou_9',
 'spl_ic_mou_6',
 'spl_ic_mou_7',
 'spl_ic_mou_8',
 'spl_ic_mou_9',
 'isd_ic_mou_6',
 'isd_ic_mou_7',
 'isd_ic_mou_8',
 'isd_ic_mou_9',
 'ic_others_6',
 'ic_others_7',
 'ic_others_8',
 'ic_others_9',
 'total_rech_num_6',
 'total_rech_num_7',
 'total_rech_num_8',
 'total_rech_num_9',
 'total_rech_amt_6',
 'total_rech_amt_7',
 'total_rech_amt_8',
 'total_rech_amt_9',
 'max_rech_amt_6',
 'max_rech_amt_7',
 'max_rech_amt_8',
 'max_rech_amt_9',
 'date_of_last_rech_6',
 'date_of_last_rech_7',
 'date_of_last_rech_8',
 'date_of_last_rech_9',
 'last_day_rch_amt_6',
 'last_day_rch_amt_7',
 'last_day_rch_amt_8',
 'last_day_rch_amt_9',
 'date_of_last_rech_data_6',
 'date_of_last_rech_data_7',
 'date_of_last_rech_data_8',
 'date_of_last_rech_data_9',
 'total_rech_data_6',
 'total_rech_data_7',
 'total_rech_data_8',
 'total_rech_data_9',
 'max_rech_data_6',
 'max_rech_data_7',
 'max_rech_data_8',
 'max_rech_data_9',
 'count_rech_2g_6',
 'count_rech_2g_7',
 'count_rech_2g_8',
 'count_rech_2g_9',
 'count_rech_3g_6',
 'count_rech_3g_7',
 'count_rech_3g_8',
 'count_rech_3g_9',
 'av_rech_amt_data_6',
 'av_rech_amt_data_7',
 'av_rech_amt_data_8',
 'av_rech_amt_data_9',
 'vol_2g_mb_6',
 'vol_2g_mb_7',
 'vol_2g_mb_8',
 'vol_2g_mb_9',
 'vol_3g_mb_6',
 'vol_3g_mb_7',
 'vol_3g_mb_8',
 'vol_3g_mb_9',
 'arpu_3g_6',
 'arpu_3g_7',
 'arpu_3g_8',
 'arpu_3g_9',
 'arpu_2g_6',
 'arpu_2g_7',
 'arpu_2g_8',
 'arpu_2g_9',
 'night_pck_user_6',
 'night_pck_user_7',
 'night_pck_user_8',
 'night_pck_user_9',
 'monthly_2g_6',
 'monthly_2g_7',
 'monthly_2g_8',
 'monthly_2g_9',
 'sachet_2g_6',
 'sachet_2g_7',
 'sachet_2g_8',
 'sachet_2g_9',
 'monthly_3g_6',
 'monthly_3g_7',
 'monthly_3g_8',
 'monthly_3g_9',
 'sachet_3g_6',
 'sachet_3g_7',
 'sachet_3g_8',
 'sachet_3g_9',
 'fb_user_6',
 'fb_user_7',
 'fb_user_8',
 'fb_user_9',
 'aon',
 'aug_vbc_3g',
 'jul_vbc_3g',
 'jun_vbc_3g',
 'sep_vbc_3g',
 'Usage'],axis=1)


tele1.head()


tele1.shape

(99999, 365)


## droping missing values which are more then 75%
tele2 = tele1.dropna(thresh=75000,axis=1)


tele2.shape

(99999, 256)


list(tele2.isnull().sum())

[0,
 0,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 0,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 0,
 1099,
 1099,
 1099,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 0,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 0,
 1099,
 1099,
 1099,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 0,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 1099,
 0,
 1099,
 1099,
 1099,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 1593,
 11090,
 8224,
 14938,
 10956,
 10442,
 5668,
 12297,
 9094,
 8717,
 20314,
 4750,
 810,
 4692,
 4692,
 16041,
 5622,
 17608,
 12683,
 23357,
 17294,
 16323,
 11348,
 19244,
 13685,
 12866,
 9345,
 3622,
 13632,
 13632,
 0,
 5378,
 5378,
 5378,
 5378,
 5378,
 5378,
 5378,
 5378,
 5378,
 5378,
 5378,
 5378,
 5378,
 5378,
 5378,
 5378,
 5378,
 0,
 5378,
 5378,
 5378,
 5378,
 5378,
 5378,
 5378,
 5378,
 5378,
 0,
 5378,
 5378,
 5378,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0]


def fill_na_num(data):
    for col in list(data.columns):
        data=data.fillna(data[col].mean())
        data=data.fillna(0)
        return data


# filling missing values with mean
tele3 = fill_na_num(tele2)


list(tele3.isnull().sum())

[0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0]


tele3.head()


tele3["churn_flag"]=pd.to_numeric(tele3["churn_flag"],errors='coerce')


tele_data = tele3.drop(['mobile_number'],axis=1)


tele_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 99999 entries, 0 to 99998
Columns: 255 entries, min_arpu to churn_flag
dtypes: float64(222), int64(33)
memory usage: 194.5 MB


Y=tele_data["churn_flag"]


Y.value_counts()

0    89808
1    10191
Name: churn_flag, dtype: int64


Y.head()

0    1
1    0
2    0
3    0
4    0
Name: churn_flag, dtype: int64


X = tele_data.drop(['churn_flag'],axis=1)


X.shape

(99999, 254)


X.head()


X.describe().to_csv("check.csv")


pd.set_option("display.max_rows",1000)


# correlation of valriables
X.corr()


x=X.drop(["sum_std_og_t2c_mou","sum_std_ic_t2o_mou","mom_arpu",
"mom_onnet_mou",
"mom_ofnet_mou",
"mom_loc_og_t2t_mou",
"mom_loc_og_t2m_mou",
"mom_loc_og_mou",
"mom_total_og_mou",
"mom_loc_ic_t2t_mou",
"mom_loc_ic_t2m_mou",
"mom_loc_ic_mou",
"mom_std_ic_mou",
"mom_total_ic_mou",
"mom_total_rech_num",
"mom_total_rech_amt",
"mom_max_rech_amt",
"mom_last_day_rch_amt","min_onnet_mou",
"min_ofnet_mou",
"min_roam_ic_mou",
"min_roam_og_mou",
"min_loc_og_t2t_mou",
"min_loc_og_t2m_mou",
"min_loc_og_t2f_mou",
"min_loc_og_t2c_mou",
"min_loc_og_mou",
"min_std_og_t2t_mou",
"min_std_og_t2m_mou",
"min_std_og_t2f_mou",
"min_std_og_t2c_mou",
"min_isd_og_mou",
"min_std_og_mou",
"min_spl_og_mou",
"min_og_others",
"min_loc_ic_t2t_mou",
"min_loc_ic_t2m_mou",
"min_loc_ic_t2f_mou",
"min_loc_ic_mou",
"min_std_ic_t2t_mou",
"min_std_ic_t2m_mou",
"min_std_ic_t2f_mou",
"min_std_ic_t2o_mou",
"min_std_ic_mou",
"min_spl_ic_mou",
"min_isd_ic_mou",
"min_ic_others",
"max_onnet_mou",
"max_ofnet_mou",
"max_roam_ic_mou",
"max_roam_og_mou",
"max_loc_og_t2t_mou",
"max_loc_og_t2m_mou",
"max_loc_og_t2f_mou",
"max_loc_og_t2c_mou",
"max_loc_og_mou",
"max_std_og_t2t_mou",
"max_std_og_t2m_mou",
"max_std_og_t2f_mou",
"max_std_og_t2c_mou",
"max_isd_og_mou",
"max_std_og_mou",
"max_spl_og_mou",
"max_og_others",
"max_loc_ic_t2t_mou",
"max_loc_ic_t2m_mou",
"max_loc_ic_t2f_mou",
"max_loc_ic_mou",
"max_std_ic_t2t_mou",
"max_std_ic_t2m_mou",
"max_std_ic_t2f_mou",
"max_std_ic_t2o_mou",
"max_std_ic_mou",
"max_spl_ic_mou",
"max_isd_ic_mou",
"max_ic_others","onnet_mou_inlast",
"ofnet_mou_inlast",
"roam_ic_mou_inlast",
"roam_og_mou_inlast",
"loc_og_t2t_mou_inlast",
"loc_og_t2m_mou_inlast",
"loc_og_t2f_mou_inlast",
"loc_og_t2c_mou_inlast",
"loc_og_mou_inlast",
"std_og_t2t_mou_inlast",
"std_og_t2m_mou_inlast",
"std_og_t2f_mou_inlast",
"std_og_t2c_mou_inlast",
"std_og_mou_inlast",
"isd_og_mou_inlast",
"spl_og_mou_inlast",
"og_others_inlast",
"loc_ic_t2t_mou_inlast",
"loc_ic_t2m_mou_inlast",
"loc_ic_t2f_mou_inlast",
"loc_ic_mou_inlast",
"std_ic_t2t_mou_inlast",
"std_ic_t2m_mou_inlast",
"std_ic_t2f_mou_inlast",
"std_ic_t2o_mou_inlast",
"std_ic_mou_inlast",
"spl_ic_mou_inlast",
"isd_ic_mou_inlast",
"ic_others_inlast","sum_arpu",
"sum_total_rech_num",
"sum_vol_3g_mb",
"sum_sachet_2g",
"sum_monthly_3g",
"sum_vbc_3g",
"mean_onnet_mou",
"mean_ofnet_mou",
"mean_roam_ic_mou",
"mean_roam_og_mou",
"mean_loc_og_t2t_mou",
"mean_loc_og_t2m_mou",
"mean_loc_og_t2f_mou",
"mean_loc_og_t2c_mou",
"mean_loc_og_mou",
"mean_std_og_t2t_mou",
"mean_std_og_t2m_mou",
"mean_std_og_t2f_mou",
"mean_std_og_t2c_mou",
"mean_isd_og_mou",
"mean_std_og_mou",
"mean_spl_og_mou",
"mean_og_others",
"mean_loc_ic_t2t_mou",
"mean_loc_ic_t2m_mou",
"mean_loc_ic_t2f_mou",
"mean_loc_ic_mou",
"mean_std_ic_t2t_mou",
"mean_std_ic_t2m_mou",
"mean_std_ic_t2f_mou",
"mean_std_ic_t2o_mou",
"mean_std_ic_mou",
"mean_spl_ic_mou",
"mean_isd_ic_mou",
"mean_ic_others",
 "mean_vol_3g_mb",
"mean_sachet_2g",
"mean_monthly_3g",
"mean_vbc_3g","sum_total_og_mou",
"sum_total_ic_mou",
"sum_total_rech_amt",
"sum_max_rech_amt",
"sum_last_day_rch_amt",
"sum_vol_2g_mb",
"sum_monthly_2g",
"sum_sachet_3g",
"moq_total_rech_amt","max_arpu","min_total_rech_amt"
],axis=1)


x.shape

(99999, 99)


x.corr().to_csv("check1.csv")


from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn import tree , metrics, preprocessing
import statsmodels.api as sm


##logistic regression
X_train, X_test, Y_train, Y_test = train_test_split( x, Y, test_size = 0.3, random_state = 100 )


print(X_train.shape)
print(X_test.shape)

(69999, 99)
(30000, 99)


lg = sm.Logit(Y_train,sm.add_constant(X_train))


lg=lg.fit()

Optimization terminated successfully.
         Current function value: 0.175981
         Iterations 11


lg.summary()


def get_predictions(Y_train,model):
    y_pred_df = pd.DataFrame({'actual':Y_train,
                             'predicted_prob':lg.predict(sm.add_constant(X_train))})
    return y_pred_df


y_pred_df_train = get_predictions(Y_train,lg)


y_pred_df_train.head()


y_pred_df_train["pred_class_train"]= y_pred_df_train.predicted_prob.map(lambda X:1 if X>0.5 else 0)


y_pred_df_train.head()


pd.crosstab(y_pred_df_train['actual'],y_pred_df_train['pred_class_train'] )


cm=metrics.confusion_matrix(y_pred_df_train["actual"], y_pred_df_train["pred_class_train"])
sn.heatmap(cm,annot=True, fmt='.2f', xticklabels=['Default','Non-Default'], yticklabels=["Default","Non-Default"])
plt.ylabel('True label')
plt.xlabel('Predicted Label')
plt.show()


print( 'Total Accuracy : ',np.round( metrics.accuracy_score( y_pred_df_train["actual"], y_pred_df_train["pred_class_train"]  ), 2 ) )
print( 'Precision : ',np.round( metrics.precision_score(y_pred_df_train["actual"], y_pred_df_train["pred_class_train"]  ), 2 ) )
print( 'Recall : ',np.round( metrics.recall_score(y_pred_df_train["actual"],y_pred_df_train["pred_class_train"]  ), 2 ) )

cm1 = metrics.confusion_matrix( y_pred_df_train["actual"], y_pred_df_train["pred_class_train"] , [1,0] )

sensitivity = cm1[0,0]/(cm1[0,0]+cm1[0,1])
print('Sensitivity : ', round( sensitivity, 2) )

specificity = cm1[1,1]/(cm1[1,0]+cm1[1,1])
print('Specificity : ', round( specificity, 2 ) )

Total Accuracy :  0.94
Precision :  0.76
Recall :  0.62

C:\Users\hp\anaconda3\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
  warnings.warn(f"Pass {args_msg} as keyword args. From version "

Sensitivity :  0.62
Specificity :  0.98


auc_score = metrics.roc_auc_score( y_pred_df_train["actual"], y_pred_df_train["pred_class_train"]  )
round( float( auc_score ), 2 )

0.8


def draw_roc( actual, probs ):
    fpr, tpr, thresholds = metrics.roc_curve( actual, probs,
                                              drop_intermediate = False )
    auc_score = metrics.roc_auc_score( actual, probs )
    plt.figure(figsize=(6, 4))
    plt.plot( fpr, tpr, label='ROC curve (area = %0.2f)' % auc_score )
    plt.plot([0, 1], [0, 1], 'k--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate or [1 - True Negative Rate]')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic example')
    plt.legend(loc="lower right")
    plt.show()

    return fpr, tpr, thresholds


fpr, tpr, thresholds = draw_roc(  y_pred_df_train["actual"], y_pred_df_train["pred_class_train"] )


## test data
def get_predictions(Y_test,model):
    y_pred_df = pd.DataFrame({'actual':Y_test,
                             'predicted_prob':lg.predict(sm.add_constant(X_test))})
    return y_pred_df


y_pred_df_test = get_predictions(Y_test,lg)


y_pred_df_test.head()


y_pred_df_test["pred_class_test"]= y_pred_df_test.predicted_prob.map(lambda X:1 if X>0.5 else 0)


y_pred_df_test.head()


pd.crosstab(y_pred_df_test["actual"],y_pred_df_test["pred_class_test"])


%matplotlib inline 

from IPython.display import Image
import matplotlib as mlp
import matplotlib.pyplot as plt
import seaborn as sn
!pip install graphviz
import graphviz
os.environ["PATH"] += os.pathsep + 'C:\\Users\\hp\\Downloads\\graphviz-2.38\\release\\bin'

Requirement already satisfied: graphviz in c:\users\hp\anaconda3\lib\site-packages (0.16)


cm=metrics.confusion_matrix(y_pred_df_test["actual"], y_pred_df_test["pred_class_test"])
sn.heatmap(cm,annot=True, fmt='.2f', xticklabels=['Default','Non-Default'], yticklabels=["Default","Non-Default"])
plt.ylabel('True label')
plt.xlabel('Predicted Label')
plt.show()


print( 'Total Accuracy : ',np.round( metrics.accuracy_score( y_pred_df_test["actual"], y_pred_df_test["pred_class_test"]  ), 2 ) )
print( 'Precision : ',np.round( metrics.precision_score(y_pred_df_test["actual"], y_pred_df_test["pred_class_test"]  ), 2 ) )
print( 'Recall : ',np.round( metrics.recall_score(y_pred_df_test["actual"],y_pred_df_test["pred_class_test"]  ), 2 ) )

cm1 = metrics.confusion_matrix( y_pred_df_test["actual"], y_pred_df_test["pred_class_test"] , [1,0] )

sensitivity = cm1[0,0]/(cm1[0,0]+cm1[0,1])
print('Sensitivity : ', round( sensitivity, 2) )

specificity = cm1[1,1]/(cm1[1,0]+cm1[1,1])
print('Specificity : ', round( specificity, 2 ) )

Total Accuracy :  0.94
Precision :  0.74
Recall :  0.61

C:\Users\hp\anaconda3\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
  warnings.warn(f"Pass {args_msg} as keyword args. From version "

Sensitivity :  0.61
Specificity :  0.98


auc_score = metrics.roc_auc_score( y_pred_df_test["actual"], y_pred_df_test["pred_class_test"]  )
round( float( auc_score ), 2 )

0.79


fpr, tpr, thresholds = draw_roc(  y_pred_df_test["actual"], y_pred_df_test["pred_class_test"] )


import pickle
filename = "logistic_churn.sav"
pickle.dump(lg, open(filename, 'wb'))


## decision tree model
from sklearn.tree import DecisionTreeClassifier
clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0) 
clf.fit(X_train, Y_train)

DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)


feature_importances = pd.DataFrame(clf.feature_importances_,
                                   index = X_train.columns,
                                    columns=['importance']).sort_values('importance', 
                                                                        ascending=False)


feature_importances


importances = clf.feature_importances_


feature_importance = clf.feature_importances_
print (clf.feature_importances_)
feat_importances = pd.Series(clf.feature_importances_, index=X_train.columns)
feat_importances = feat_importances.nlargest(10) # specify how many you need to see
feat_importances.plot(kind='barh' , figsize=(10,10))

[0.00000000e+00 0.00000000e+00 2.90610801e-02 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 1.83843497e-02 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 3.23856846e-02 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 1.80533303e-02 5.08874796e-04 8.34795973e-01
 0.00000000e+00 0.00000000e+00 0.00000000e+00 6.68107075e-02
 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 0.00000000e+00]

<AxesSubplot:>


dot_data = tree.export_graphviz(clf, out_file=None, feature_names = x.columns)
graph = graphviz.Source(dot_data)  
graph


y_pred = clf.predict(X_test)


y_pred

array([0, 0, 0, ..., 0, 0, 0], dtype=int64)


final_model_predictions = pd.DataFrame({'Actual':Y_test, 'predictions':y_pred})


final_model_predictions.head()


count_misclassified = (Y_test != y_pred).sum()
print('Misclassified samples: {}'.format(count_misclassified))
accuracy = metrics.accuracy_score(Y_test, y_pred)
print('Accuracy: {:.2f}'.format(accuracy))

Misclassified samples: 1884
Accuracy: 0.94


y_pred_prob = clf.predict_proba(X_test)


y_pred_prob

array([[0.98843384, 0.01156616],
       [0.98843384, 0.01156616],
       [0.98843384, 0.01156616],
       ...,
       [0.98843384, 0.01156616],
       [0.98843384, 0.01156616],
       [0.94819892, 0.05180108]])


y_pred_prob = clf.predict_proba(X_test)[:, 1]


y_pred_prob

array([0.01156616, 0.01156616, 0.01156616, ..., 0.01156616, 0.01156616,
       0.05180108])


final_model_predictions['Predicted_prob'] = y_pred_prob


final_model_predictions.head()


print( 'Total Accuracy : ',np.round( metrics.accuracy_score( final_model_predictions.Actual, final_model_predictions.predictions  ), 2 ) )
print( 'Precision : ',np.round( metrics.precision_score( final_model_predictions.Actual, final_model_predictions.predictions  ), 2 ) )
print( 'Recall : ',np.round( metrics.recall_score(final_model_predictions.Actual, final_model_predictions.predictions  ), 2 ) )

cm1 = metrics.confusion_matrix( final_model_predictions.Actual, final_model_predictions.predictions , [1,0] )

sensitivity = cm1[0,0]/(cm1[0,0]+cm1[0,1])
print('Sensitivity : ', round( sensitivity, 2) )

specificity = cm1[1,1]/(cm1[1,0]+cm1[1,1])
print('Specificity : ', round( specificity, 2 ) )

Total Accuracy :  0.94
Precision :  0.72
Recall :  0.62

C:\Users\hp\anaconda3\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
  warnings.warn(f"Pass {args_msg} as keyword args. From version "

Sensitivity :  0.62
Specificity :  0.97


def draw_cm( actual, predicted ):
    cm = metrics.confusion_matrix( actual, predicted, [1,0] )
    sn.heatmap(cm, annot=True,  fmt='.2f', xticklabels = ["Default", "No Default"] , yticklabels = ["Default", "No Default"] )
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.show()


draw_cm( final_model_predictions.Actual, final_model_predictions.predictions )

C:\Users\hp\anaconda3\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
  warnings.warn(f"Pass {args_msg} as keyword args. From version "


auc_score = metrics.roc_auc_score( final_model_predictions.Actual, final_model_predictions.Predicted_prob  )
round( float( auc_score ), 2 )

0.89


fpr, tpr, thresholds = draw_roc( final_model_predictions.Actual, final_model_predictions.Predicted_prob )


y_pred = clf.predict(X_train)


final_model_predictions1 = pd.DataFrame({'Actual':Y_train, 'predictions':y_pred})


final_model_predictions1.head()


y_pred_prob = clf.predict_proba(X_train)


y_pred_prob = clf.predict_proba(X_train)[:, 1]


final_model_predictions1['Predicted_prob'] = y_pred_prob


final_model_predictions1.head()


print( 'Total Accuracy : ',np.round( metrics.accuracy_score( final_model_predictions1.Actual, final_model_predictions1.predictions  ), 2 ) )
print( 'Precision : ',np.round( metrics.precision_score( final_model_predictions1.Actual, final_model_predictions1.predictions  ), 2 ) )
print( 'Recall : ',np.round( metrics.recall_score(final_model_predictions1.Actual, final_model_predictions1.predictions  ), 2 ) )

cm1 = metrics.confusion_matrix( final_model_predictions1.Actual, final_model_predictions1.predictions , [1,0] )

sensitivity = cm1[0,0]/(cm1[0,0]+cm1[0,1])
print('Sensitivity : ', round( sensitivity, 2) )

specificity = cm1[1,1]/(cm1[1,0]+cm1[1,1])
print('Specificity : ', round( specificity, 2 ) )

Total Accuracy :  0.94
Precision :  0.75
Recall :  0.64

C:\Users\hp\anaconda3\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
  warnings.warn(f"Pass {args_msg} as keyword args. From version "

Sensitivity :  0.64
Specificity :  0.98


draw_cm( final_model_predictions1.Actual, final_model_predictions1.predictions )

C:\Users\hp\anaconda3\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
  warnings.warn(f"Pass {args_msg} as keyword args. From version "


auc_score = metrics.roc_auc_score( final_model_predictions1.Actual, final_model_predictions1.Predicted_prob  )
round( float( auc_score ), 2 )

0.9


fpr, tpr, thresholds = draw_roc( final_model_predictions1.Actual, final_model_predictions1.Predicted_prob )


import pickle
filename = "Decision_Tree_churn.sav"
pickle.dump(clf, open(filename, 'wb'))


## random forest model
from sklearn.ensemble import RandomForestClassifier
Rf = RandomForestClassifier(n_estimators = 1000, random_state = 42,max_features=4)
Rf.fit(X_train, Y_train)

RandomForestClassifier(max_features=4, n_estimators=1000, random_state=42)


len(Rf.estimators_)

1000


from sklearn.tree import export_graphviz
estimator = Rf.estimators_[999] # number of trees to visualise
# Export as dot file
export_graphviz(estimator, out_file='RandomForest.dot', 
                feature_names = X_train.columns,
                #class_names = y_train.columns,
                rounded = True, proportion = False, 
                precision = 2, filled = True)


from subprocess import call
!dot -Tpng RandomForest.dot -o tree_limited.png -Gdpi=200
from IPython.display import Image
Image(filename = 'tree_limited.png')

dot: graph is too large for cairo-renderer bitmaps. Scaling by 0.079552 to fit


feature_importances = pd.DataFrame(Rf.feature_importances_,
                                   index = X_train.columns,
                                    columns=['importance']).sort_values('importance', 
                                                                        ascending=False)


feature_importances


y_pred = Rf.predict(X_test)


y_pred

array([0, 0, 0, ..., 0, 0, 0], dtype=int64)


final_model_predictions2 = pd.DataFrame({'Actual':Y_test, 'predictions':y_pred})


final_model_predictions2.head()


count_misclassified = (Y_test != y_pred).sum()
print('Misclassified samples: {}'.format(count_misclassified))
accuracy = metrics.accuracy_score(Y_test, y_pred)
print('Accuracy: {:.2f}'.format(accuracy))

Misclassified samples: 1814
Accuracy: 0.94


y_pred_prob = Rf.predict_proba(X_test)


y_pred_prob

array([[0.991, 0.009],
       [0.983, 0.017],
       [0.878, 0.122],
       ...,
       [0.945, 0.055],
       [0.986, 0.014],
       [0.949, 0.051]])


y_pred_prob = Rf.predict_proba(X_test)[:, 1]


final_model_predictions2['Predicted_prob'] = y_pred_prob


final_model_predictions2.head()


def draw_cm( actual, predicted ):
    cm = metrics.confusion_matrix( actual, predicted, [1,0] )
    sn.heatmap(cm, annot=True,  fmt='.2f', xticklabels = ["Default", "No Default"] , yticklabels = ["Default", "No Default"] )
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.show()


draw_cm( final_model_predictions2.Actual, final_model_predictions2.predictions )

C:\Users\hp\anaconda3\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
  warnings.warn(f"Pass {args_msg} as keyword args. From version "


print( 'Total Accuracy : ',np.round( metrics.accuracy_score( final_model_predictions2.Actual, final_model_predictions2.predictions  ), 2 ) )
print( 'Precision : ',np.round( metrics.precision_score( final_model_predictions2.Actual, final_model_predictions2.predictions  ), 2 ) )
print( 'Recall : ',np.round( metrics.recall_score(final_model_predictions2.Actual, final_model_predictions2.predictions  ), 2 ) )

cm1 = metrics.confusion_matrix( final_model_predictions2.Actual, final_model_predictions2.predictions , [1,0] )

sensitivity = cm1[0,0]/(cm1[0,0]+cm1[0,1])
print('Sensitivity : ', round( sensitivity, 2) )

specificity = cm1[1,1]/(cm1[1,0]+cm1[1,1])
print('Specificity : ', round( specificity, 2 ) )

Total Accuracy :  0.94
Precision :  0.76
Recall :  0.59
Sensitivity :  0.59
Specificity :  0.98

C:\Users\hp\anaconda3\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
  warnings.warn(f"Pass {args_msg} as keyword args. From version "


auc_score = metrics.roc_auc_score( final_model_predictions2.Actual, final_model_predictions2.Predicted_prob  )
round( float( auc_score ), 2 )

0.93


def draw_roc( actual, probs ):
    fpr, tpr, thresholds = metrics.roc_curve( actual, probs,
                                              drop_intermediate = False )
    auc_score = metrics.roc_auc_score( actual, probs )
    plt.figure(figsize=(6, 4))
    plt.plot( fpr, tpr, label='ROC curve (area = %0.2f)' % auc_score )
    plt.plot([0, 1], [0, 1], 'k--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate or [1 - True Negative Rate]')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic example')
    plt.legend(loc="lower right")
    plt.show()

    return fpr, tpr, thresholds


fpr, tpr, thresholds = draw_roc( final_model_predictions2.Actual, final_model_predictions2.Predicted_prob )


y_pred = Rf.predict(X_train)


model_predictions2 = pd.DataFrame({'Actual':Y_train, 'predictions':y_pred})


model_predictions2.head()


count_misclassified = (Y_train != y_pred).sum()
print('Misclassified samples: {}'.format(count_misclassified))
accuracy = metrics.accuracy_score(Y_train, y_pred)
print('Accuracy: {:.2f}'.format(accuracy))

Misclassified samples: 0
Accuracy: 1.00


y_pred_prob = Rf.predict_proba(X_train)


y_pred_prob = Rf.predict_proba(X_train)[:, 1]


model_predictions2['Predicted_prob'] = y_pred_prob


model_predictions2.head()


draw_cm( model_predictions2.Actual, model_predictions2.predictions )

C:\Users\hp\anaconda3\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
  warnings.warn(f"Pass {args_msg} as keyword args. From version "


print( 'Total Accuracy : ',np.round( metrics.accuracy_score( model_predictions2.Actual, model_predictions2.predictions  ), 2 ) )
print( 'Precision : ',np.round( metrics.precision_score( model_predictions2.Actual, model_predictions2.predictions  ), 2 ) )
print( 'Recall : ',np.round( metrics.recall_score(model_predictions2.Actual, model_predictions2.predictions  ), 2 ) )

cm1 = metrics.confusion_matrix( model_predictions2.Actual, model_predictions2.predictions , [1,0] )

sensitivity = cm1[0,0]/(cm1[0,0]+cm1[0,1])
print('Sensitivity : ', round( sensitivity, 2) )

specificity = cm1[1,1]/(cm1[1,0]+cm1[1,1])
print('Specificity : ', round( specificity, 2 ) )

Total Accuracy :  1.0
Precision :  1.0
Recall :  1.0

C:\Users\hp\anaconda3\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
  warnings.warn(f"Pass {args_msg} as keyword args. From version "

Sensitivity :  1.0
Specificity :  1.0


auc_score = metrics.roc_auc_score( model_predictions2.Actual, model_predictions2.Predicted_prob  )
round( float( auc_score ), 2 )

1.0


fpr, tpr, thresholds = draw_roc( model_predictions2.Actual, model_predictions2.Predicted_prob )


import pickle
filename = "Random_Forest_churn.sav"
pickle.dump(clf, open(filename, 'wb'))


##  GradientBoosting
from sklearn.ensemble import GradientBoostingClassifier
GB= GradientBoostingClassifier(learning_rate=0.01,random_state=1)
GB.fit(X_train, Y_train)

GradientBoostingClassifier(learning_rate=0.01, random_state=1)


GB.estimators_

array([[DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)],
       [DecisionTreeRegressor(criterion='friedman_mse', max_depth=3,
                              random_state=RandomState(MT19937) at 0x2003669D540)]],
      dtype=object)


feature_importances = pd.DataFrame(GB.feature_importances_,
                                   index = X_train.columns,
                                    columns=['importance']).sort_values('importance', 
                                                                        ascending=False)


feature_importances


y_pred = GB.predict(X_test)


y_pred

array([0, 0, 0, ..., 0, 0, 0], dtype=int64)


final_model_predictions3 = pd.DataFrame({'Actual':Y_test, 'predictions':y_pred})


final_model_predictions3.head()


count_misclassified = (Y_test != y_pred).sum()
print('Misclassified samples: {}'.format(count_misclassified))
accuracy = metrics.accuracy_score(Y_test, y_pred)
print('Accuracy: {:.2f}'.format(accuracy))

Misclassified samples: 2039
Accuracy: 0.93


y_pred_prob = clf.predict_proba(X_test)


y_pred_prob = clf.predict_proba(X_test)[:, 1]


final_model_predictions3['Predicted_prob'] = y_pred_prob


final_model_predictions3.head()


print( 'Total Accuracy : ',np.round( metrics.accuracy_score( final_model_predictions3.Actual, final_model_predictions3.predictions  ), 2 ) )
print( 'Precision : ',np.round( metrics.precision_score( final_model_predictions3.Actual, final_model_predictions3.predictions  ), 2 ) )
print( 'Recall : ',np.round( metrics.recall_score(final_model_predictions3.Actual, final_model_predictions3.predictions  ), 2 ) )

cm1 = metrics.confusion_matrix( final_model_predictions3.Actual, final_model_predictions3.predictions , [1,0] )

sensitivity = cm1[0,0]/(cm1[0,0]+cm1[0,1])
print('Sensitivity : ', round( sensitivity, 2) )

specificity = cm1[1,1]/(cm1[1,0]+cm1[1,1])
print('Specificity : ', round( specificity, 2 ) )

Total Accuracy :  0.93
Precision :  0.83
Recall :  0.42

C:\Users\hp\anaconda3\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
  warnings.warn(f"Pass {args_msg} as keyword args. From version "

Sensitivity :  0.42
Specificity :  0.99


draw_cm( final_model_predictions3.Actual, final_model_predictions3.predictions )

C:\Users\hp\anaconda3\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
  warnings.warn(f"Pass {args_msg} as keyword args. From version "


auc_score = metrics.roc_auc_score( final_model_predictions3.Actual, final_model_predictions3.Predicted_prob  )
round( float( auc_score ), 2 )

0.89


fpr, tpr, thresholds = draw_roc( final_model_predictions3.Actual, final_model_predictions3.Predicted_prob )


# train_data
y_pred = GB.predict(X_train)


y_pred

array([0, 0, 0, ..., 0, 0, 0], dtype=int64)


final_model_predictions4 = pd.DataFrame({'Actual':Y_train, 'predictions':y_pred})


final_model_predictions4.head()


count_misclassified = (Y_train != y_pred).sum()
print('Misclassified samples: {}'.format(count_misclassified))
accuracy = metrics.accuracy_score(Y_train, y_pred)
print('Accuracy: {:.2f}'.format(accuracy))

Misclassified samples: 4686
Accuracy: 0.93


y_pred_prob = clf.predict_proba(X_train)


y_pred_prob = clf.predict_proba(X_train)[:, 1]


final_model_predictions4['Predicted_prob'] = y_pred_prob


final_model_predictions4.head()


draw_cm( final_model_predictions4.Actual, final_model_predictions4.predictions )

C:\Users\hp\anaconda3\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
  warnings.warn(f"Pass {args_msg} as keyword args. From version "


print( 'Total Accuracy : ',np.round( metrics.accuracy_score( final_model_predictions4.Actual, final_model_predictions4.predictions  ), 2 ) )
print( 'Precision : ',np.round( metrics.precision_score( final_model_predictions4.Actual, final_model_predictions4.predictions  ), 2 ) )
print( 'Recall : ',np.round( metrics.recall_score(final_model_predictions4.Actual, final_model_predictions4.predictions  ), 2 ) )

cm1 = metrics.confusion_matrix( final_model_predictions4.Actual, final_model_predictions4.predictions , [1,0] )

sensitivity = cm1[0,0]/(cm1[0,0]+cm1[0,1])
print('Sensitivity : ', round( sensitivity, 2) )

specificity = cm1[1,1]/(cm1[1,0]+cm1[1,1])
print('Specificity : ', round( specificity, 2 ) )

Total Accuracy :  0.93
Precision :  0.85
Recall :  0.42

C:\Users\hp\anaconda3\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
  warnings.warn(f"Pass {args_msg} as keyword args. From version "

Sensitivity :  0.42
Specificity :  0.99


auc_score = metrics.roc_auc_score( final_model_predictions4.Actual, final_model_predictions4.Predicted_prob  )
round( float( auc_score ), 2 )

0.9


fpr, tpr, thresholds = draw_roc( final_model_predictions4.Actual, final_model_predictions4.Predicted_prob )


import pickle
filename = "Gradient_Boosting_churn.sav"
pickle.dump(GB, open(filename, 'wb'))


tele["min_arpu"]= tele[['arpu_9','arpu_7','arpu_8']].apply(char_min,axis=1)
tele["min_onnet_mou"]= tele[['onnet_mou_9','onnet_mou_7','onnet_mou_8']].apply(char_min,axis=1)
tele["min_ofnet_mou"]= tele[['offnet_mou_9','offnet_mou_7','offnet_mou_8']].apply(char_min,axis=1)
tele["min_roam_ic_mou"]= tele[['roam_ic_mou_9','roam_ic_mou_7','roam_ic_mou_8']].apply(char_min,axis=1)
tele["min_roam_og_mou"]= tele[['roam_og_mou_9','roam_og_mou_7','roam_og_mou_8']].apply(char_min,axis=1)
tele["min_loc_og_t2t_mou"]= tele[['loc_og_t2t_mou_9','loc_og_t2t_mou_7','loc_og_t2t_mou_8']].apply(char_min,axis=1)
tele["min_loc_og_t2m_mou"]= tele[['loc_og_t2m_mou_9','loc_og_t2m_mou_7','loc_og_t2m_mou_8']].apply(char_min,axis=1)
tele["min_loc_og_t2f_mou"]= tele[['loc_og_t2f_mou_9','loc_og_t2f_mou_7','loc_og_t2f_mou_8']].apply(char_min,axis=1)
tele["min_loc_og_t2c_mou"]= tele[['loc_og_t2c_mou_9','loc_og_t2c_mou_7','loc_og_t2c_mou_8']].apply(char_min,axis=1)
tele["min_loc_og_mou"]= tele[['loc_og_mou_9','loc_og_mou_7','loc_og_mou_8']].apply(char_min,axis=1)
tele["min_std_og_t2t_mou"]= tele[['std_og_t2t_mou_9','std_og_t2t_mou_7','std_og_t2t_mou_8']].apply(char_min,axis=1)
tele["min_std_og_t2m_mou"]= tele[['std_og_t2m_mou_9','std_og_t2m_mou_7','std_og_t2m_mou_8']].apply(char_min,axis=1)
tele["min_std_og_t2f_mou"]= tele[['std_og_t2f_mou_9','std_og_t2f_mou_7','std_og_t2f_mou_8']].apply(char_min,axis=1)
tele["min_std_og_t2c_mou"]= tele[['std_og_t2c_mou_9','std_og_t2c_mou_7','std_og_t2c_mou_8']].apply(char_min,axis=1)
tele["min_isd_og_mou"]= tele[['isd_og_mou_9','isd_og_mou_7','isd_og_mou_8']].apply(char_min,axis=1)
tele["min_std_og_mou"]= tele[['std_og_mou_9','std_og_mou_7','std_og_mou_8']].apply(char_min,axis=1)
tele["min_spl_og_mou"]= tele[['spl_og_mou_9','spl_og_mou_7','spl_og_mou_8']].apply(char_min,axis=1)
tele["min_og_others"]= tele[['og_others_9','og_others_7','og_others_8']].apply(char_min,axis=1)
tele["min_total_og_mou"]= tele[['total_og_mou_9','total_og_mou_7','total_og_mou_8']].apply(char_min,axis=1)
tele["min_loc_ic_t2t_mou"]= tele[['loc_ic_t2t_mou_9','loc_ic_t2t_mou_7','loc_ic_t2t_mou_8']].apply(char_min,axis=1)
tele["min_loc_ic_t2m_mou"]= tele[['loc_ic_t2m_mou_9','loc_ic_t2m_mou_7','loc_ic_t2m_mou_8']].apply(char_min,axis=1)
tele["min_loc_ic_t2f_mou"]= tele[['loc_ic_t2f_mou_9','loc_ic_t2f_mou_7','loc_ic_t2f_mou_8']].apply(char_min,axis=1)
tele["min_loc_ic_mou"]= tele[['loc_ic_mou_9','loc_ic_mou_7','loc_ic_mou_8']].apply(char_min,axis=1)
tele["min_std_ic_t2t_mou"]= tele[['std_ic_t2t_mou_9','std_ic_t2t_mou_7','std_ic_t2t_mou_8']].apply(char_min,axis=1)
tele["min_std_ic_t2m_mou"]= tele[['std_ic_t2m_mou_9','std_ic_t2m_mou_7','std_ic_t2m_mou_8']].apply(char_min,axis=1)
tele["min_std_ic_t2f_mou"]= tele[['std_ic_t2f_mou_9','std_ic_t2f_mou_7','std_ic_t2f_mou_8']].apply(char_min,axis=1)
tele["min_std_ic_t2o_mou"]= tele[['std_ic_t2o_mou_9','std_ic_t2o_mou_7','std_ic_t2o_mou_8']].apply(char_min,axis=1)
tele["min_std_ic_mou"]= tele[['std_ic_mou_9','std_ic_mou_7','std_ic_mou_8']].apply(char_min,axis=1)
tele["min_total_ic_mou"]= tele[['total_ic_mou_9','total_ic_mou_7','total_ic_mou_8']].apply(char_min,axis=1)
tele["min_spl_ic_mou"]= tele[['spl_ic_mou_9','spl_ic_mou_7','spl_ic_mou_8']].apply(char_min,axis=1)
tele["min_isd_ic_mou"]= tele[['isd_ic_mou_9','isd_ic_mou_7','isd_ic_mou_8']].apply(char_min,axis=1)
tele["min_ic_others"]= tele[['ic_others_9','ic_others_7','ic_others_8']].apply(char_min,axis=1)
tele["min_total_rech_num"]= tele[['total_rech_num_9','total_rech_num_7','total_rech_num_8']].apply(char_min,axis=1)
tele["min_total_rech_amt"]= tele[['total_rech_amt_9','total_rech_amt_7','total_rech_amt_8']].apply(char_min,axis=1)
tele["min_max_rech_amt"]= tele[['max_rech_amt_9','max_rech_amt_7','max_rech_amt_8']].apply(char_min,axis=1)
tele["min_last_day_rch_amt"]= tele[['last_day_rch_amt_9','last_day_rch_amt_7','last_day_rch_amt_8']].apply(char_min,axis=1)
tele["min_total_rech_data"]= tele[['total_rech_data_9','total_rech_data_7','total_rech_data_8']].apply(char_min,axis=1)
tele["min_max_rech_data"]= tele[['max_rech_data_9','max_rech_data_7','max_rech_data_8']].apply(char_min,axis=1)
tele["min_count_rech_2g"]= tele[['count_rech_2g_9','count_rech_2g_7','count_rech_2g_8']].apply(char_min,axis=1)
tele["min_count_rech_3g"]= tele[['count_rech_3g_9','count_rech_3g_7','count_rech_3g_8']].apply(char_min,axis=1)
tele["min_av_rech_amt_data"]= tele[['av_rech_amt_data_9','av_rech_amt_data_7','av_rech_amt_data_8']].apply(char_min,axis=1)
tele["min_vol_2g_mb"]= tele[['vol_2g_mb_9','vol_2g_mb_7','vol_2g_mb_8']].apply(char_min,axis=1)
tele["min_vol_3g_mb"]= tele[['vol_3g_mb_9','vol_3g_mb_7','vol_3g_mb_8']].apply(char_min,axis=1)
tele["min_arpu_3g"]= tele[['arpu_3g_9','arpu_3g_7','arpu_3g_8']].apply(char_min,axis=1)
tele["min_arpu_2g"]= tele[['arpu_2g_9','arpu_2g_7','arpu_2g_8']].apply(char_min,axis=1)
tele["min_night_pck_user"]= tele[['night_pck_user_9','night_pck_user_7','night_pck_user_8']].apply(char_min,axis=1)
tele["min_av_rech_amt_data"]= tele[['av_rech_amt_data_9','av_rech_amt_data_7','av_rech_amt_data_8']].apply(char_min,axis=1)
tele["min_monthly_2g"]= tele[['monthly_2g_9','monthly_2g_7','monthly_2g_8']].apply(char_min,axis=1)
tele["min_sachet_2g"]= tele[['sachet_2g_9','sachet_2g_7','sachet_2g_8']].apply(char_min,axis=1)
tele["min_monthly_3g"]= tele[['monthly_3g_9','monthly_3g_7','monthly_3g_8']].apply(char_min,axis=1)
tele["min_sachet_3g"]= tele[['sachet_3g_9','sachet_3g_7','sachet_3g_8']].apply(char_min,axis=1)
tele["min_fb_user"]= tele[['fb_user_9','fb_user_7','fb_user_8']].apply(char_min,axis=1)
tele["min_vbc_3g"]= tele[['sep_vbc_3g','jul_vbc_3g','aug_vbc_3g']].apply(char_min,axis=1)


tele["max_arpu"]= tele[['arpu_9','arpu_7','arpu_8']].apply(char_max,axis=1)
tele["max_onnet_mou"]= tele[['onnet_mou_9','onnet_mou_7','onnet_mou_8']].apply(char_max,axis=1)
tele["max_ofnet_mou"]= tele[['offnet_mou_9','offnet_mou_7','offnet_mou_8']].apply(char_max,axis=1)
tele["max_roam_ic_mou"]= tele[['roam_ic_mou_9','roam_ic_mou_7','roam_ic_mou_8']].apply(char_max,axis=1)
tele["max_roam_og_mou"]= tele[['roam_og_mou_9','roam_og_mou_7','roam_og_mou_8']].apply(char_max,axis=1)
tele["max_loc_og_t2t_mou"]= tele[['loc_og_t2t_mou_9','loc_og_t2t_mou_7','loc_og_t2t_mou_8']].apply(char_max,axis=1)
tele["max_loc_og_t2m_mou"]= tele[['loc_og_t2m_mou_9','loc_og_t2m_mou_7','loc_og_t2m_mou_8']].apply(char_max,axis=1)
tele["max_loc_og_t2f_mou"]= tele[['loc_og_t2f_mou_9','loc_og_t2f_mou_7','loc_og_t2f_mou_8']].apply(char_max,axis=1)
tele["max_loc_og_t2c_mou"]= tele[['loc_og_t2c_mou_9','loc_og_t2c_mou_7','loc_og_t2c_mou_8']].apply(char_max,axis=1)
tele["max_loc_og_mou"]= tele[['loc_og_mou_9','loc_og_mou_7','loc_og_mou_8']].apply(char_max,axis=1)
tele["max_std_og_t2t_mou"]= tele[['std_og_t2t_mou_9','std_og_t2t_mou_7','std_og_t2t_mou_8']].apply(char_max,axis=1)
tele["max_std_og_t2m_mou"]= tele[['std_og_t2m_mou_9','std_og_t2m_mou_7','std_og_t2m_mou_8']].apply(char_max,axis=1)
tele["max_std_og_t2f_mou"]= tele[['std_og_t2f_mou_9','std_og_t2f_mou_7','std_og_t2f_mou_8']].apply(char_max,axis=1)
tele["max_std_og_t2c_mou"]= tele[['std_og_t2c_mou_9','std_og_t2c_mou_7','std_og_t2c_mou_8']].apply(char_max,axis=1)
tele["max_isd_og_mou"]= tele[['isd_og_mou_9','isd_og_mou_7','isd_og_mou_8']].apply(char_max,axis=1)
tele["max_std_og_mou"]= tele[['std_og_mou_9','std_og_mou_7','std_og_mou_8']].apply(char_max,axis=1)
tele["max_spl_og_mou"]= tele[['spl_og_mou_9','spl_og_mou_7','spl_og_mou_8']].apply(char_max,axis=1)
tele["max_og_others"]= tele[['og_others_9','og_others_7','og_others_8']].apply(char_max,axis=1)
tele["max_total_og_mou"]= tele[['total_og_mou_9','total_og_mou_7','total_og_mou_8']].apply(char_max,axis=1)
tele["max_loc_ic_t2t_mou"]= tele[['loc_ic_t2t_mou_9','loc_ic_t2t_mou_7','loc_ic_t2t_mou_8']].apply(char_max,axis=1)
tele["max_loc_ic_t2m_mou"]= tele[['loc_ic_t2m_mou_9','loc_ic_t2m_mou_7','loc_ic_t2m_mou_8']].apply(char_max,axis=1)
tele["max_loc_ic_t2f_mou"]= tele[['loc_ic_t2f_mou_9','loc_ic_t2f_mou_7','loc_ic_t2f_mou_8']].apply(char_max,axis=1)
tele["max_loc_ic_mou"]= tele[['loc_ic_mou_9','loc_ic_mou_7','loc_ic_mou_8']].apply(char_max,axis=1)
tele["max_std_ic_t2t_mou"]= tele[['std_ic_t2t_mou_9','std_ic_t2t_mou_7','std_ic_t2t_mou_8']].apply(char_max,axis=1)
tele["max_std_ic_t2m_mou"]= tele[['std_ic_t2m_mou_9','std_ic_t2m_mou_7','std_ic_t2m_mou_8']].apply(char_max,axis=1)
tele["max_std_ic_t2f_mou"]= tele[['std_ic_t2f_mou_9','std_ic_t2f_mou_7','std_ic_t2f_mou_8']].apply(char_max,axis=1)
tele["max_std_ic_t2o_mou"]= tele[['std_ic_t2o_mou_9','std_ic_t2o_mou_7','std_ic_t2o_mou_8']].apply(char_max,axis=1)
tele["max_std_ic_mou"]= tele[['std_ic_mou_9','std_ic_mou_7','std_ic_mou_8']].apply(char_max,axis=1)
tele["max_total_ic_mou"]= tele[['total_ic_mou_9','total_ic_mou_7','total_ic_mou_8']].apply(char_max,axis=1)
tele["max_spl_ic_mou"]= tele[['spl_ic_mou_9','spl_ic_mou_7','spl_ic_mou_8']].apply(char_max,axis=1)
tele["max_isd_ic_mou"]= tele[['isd_ic_mou_9','isd_ic_mou_7','isd_ic_mou_8']].apply(char_max,axis=1)
tele["max_ic_others"]= tele[['ic_others_9','ic_others_7','ic_others_8']].apply(char_max,axis=1)
tele["max_total_rech_num"]= tele[['total_rech_num_9','total_rech_num_7','total_rech_num_8']].apply(char_max,axis=1)
tele["max_total_rech_amt"]= tele[['total_rech_amt_9','total_rech_amt_7','total_rech_amt_8']].apply(char_max,axis=1)
tele["max_max_rech_amt"]= tele[['max_rech_amt_9','max_rech_amt_7','max_rech_amt_8']].apply(char_max,axis=1)
tele["max_last_day_rch_amt"]= tele[['last_day_rch_amt_9','last_day_rch_amt_7','last_day_rch_amt_8']].apply(char_max,axis=1)
tele["max_total_rech_data"]= tele[['total_rech_data_9','total_rech_data_7','total_rech_data_8']].apply(char_max,axis=1)
tele["max_max_rech_data"]= tele[['max_rech_data_9','max_rech_data_7','max_rech_data_8']].apply(char_max,axis=1)
tele["max_count_rech_2g"]= tele[['count_rech_2g_9','count_rech_2g_7','count_rech_2g_8']].apply(char_max,axis=1)
tele["max_count_rech_3g"]= tele[['count_rech_3g_9','count_rech_3g_7','count_rech_3g_8']].apply(char_max,axis=1)
tele["max_av_rech_amt_data"]= tele[['av_rech_amt_data_9','av_rech_amt_data_7','av_rech_amt_data_8']].apply(char_max,axis=1)
tele["max_vol_2g_mb"]= tele[['vol_2g_mb_9','vol_2g_mb_7','vol_2g_mb_8']].apply(char_max,axis=1)
tele["max_vol_3g_mb"]= tele[['vol_3g_mb_9','vol_3g_mb_7','vol_3g_mb_8']].apply(char_max,axis=1)
tele["max_arpu_3g"]= tele[['arpu_3g_9','arpu_3g_7','arpu_3g_8']].apply(char_max,axis=1)
tele["max_arpu_2g"]= tele[['arpu_2g_9','arpu_2g_7','arpu_2g_8']].apply(char_max,axis=1)
tele["max_night_pck_user"]= tele[['night_pck_user_9','night_pck_user_7','night_pck_user_8']].apply(char_max,axis=1)
tele["max_av_rech_amt_data"]= tele[['av_rech_amt_data_9','av_rech_amt_data_7','av_rech_amt_data_8']].apply(char_max,axis=1)
tele["max_monthly_2g"]= tele[['monthly_2g_9','monthly_2g_7','monthly_2g_8']].apply(char_max,axis=1)
tele["max_sachet_2g"]= tele[['sachet_2g_9','sachet_2g_7','sachet_2g_8']].apply(char_max,axis=1)
tele["max_monthly_3g"]= tele[['monthly_3g_9','monthly_3g_7','monthly_3g_8']].apply(char_max,axis=1)
tele["max_sachet_3g"]= tele[['sachet_3g_9','sachet_3g_7','sachet_3g_8']].apply(char_max,axis=1)
tele["max_fb_user"]= tele[['fb_user_9','fb_user_7','fb_user_8']].apply(char_max,axis=1)
tele["max_vbc_3g"]= tele[['sep_vbc_3g','jul_vbc_3g','aug_vbc_3g']].apply(char_max,axis=1)


tele["sum_arpu"]= tele[['arpu_9','arpu_7','arpu_8']].apply(char_sum,axis=1)
tele["sum_onnet_mou"]= tele[['onnet_mou_9','onnet_mou_7','onnet_mou_8']].apply(char_sum,axis=1)
tele["sum_ofnet_mou"]= tele[['offnet_mou_9','offnet_mou_7','offnet_mou_8']].apply(char_sum,axis=1)
tele["sum_roam_ic_mou"]= tele[['roam_ic_mou_9','roam_ic_mou_7','roam_ic_mou_8']].apply(char_sum,axis=1)
tele["sum_roam_og_mou"]= tele[['roam_og_mou_9','roam_og_mou_7','roam_og_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_og_t2t_mou"]= tele[['loc_og_t2t_mou_9','loc_og_t2t_mou_7','loc_og_t2t_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_og_t2m_mou"]= tele[['loc_og_t2m_mou_9','loc_og_t2m_mou_7','loc_og_t2m_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_og_t2f_mou"]= tele[['loc_og_t2f_mou_9','loc_og_t2f_mou_7','loc_og_t2f_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_og_t2c_mou"]= tele[['loc_og_t2c_mou_9','loc_og_t2c_mou_7','loc_og_t2c_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_og_mou"]= tele[['loc_og_mou_9','loc_og_mou_7','loc_og_mou_8']].apply(char_sum,axis=1)
tele["sum_std_og_t2t_mou"]= tele[['std_og_t2t_mou_9','std_og_t2t_mou_7','std_og_t2t_mou_8']].apply(char_sum,axis=1)
tele["sum_std_og_t2m_mou"]= tele[['std_og_t2m_mou_9','std_og_t2m_mou_7','std_og_t2m_mou_8']].apply(char_sum,axis=1)
tele["sum_std_og_t2f_mou"]= tele[['std_og_t2f_mou_9','std_og_t2f_mou_7','std_og_t2f_mou_8']].apply(char_sum,axis=1)
tele["sum_std_og_t2c_mou"]= tele[['std_og_t2c_mou_9','std_og_t2c_mou_7','std_og_t2c_mou_8']].apply(char_sum,axis=1)
tele["sum_isd_og_mou"]= tele[['isd_og_mou_9','isd_og_mou_7','isd_og_mou_8']].apply(char_sum,axis=1)
tele["sum_std_og_mou"]= tele[['std_og_mou_9','std_og_mou_7','std_og_mou_8']].apply(char_sum,axis=1)
tele["sum_spl_og_mou"]= tele[['spl_og_mou_9','spl_og_mou_7','spl_og_mou_8']].apply(char_sum,axis=1)
tele["sum_og_others"]= tele[['og_others_9','og_others_7','og_others_8']].apply(char_sum,axis=1)
tele["sum_total_og_mou"]= tele[['total_og_mou_9','total_og_mou_7','total_og_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_ic_t2t_mou"]= tele[['loc_ic_t2t_mou_9','loc_ic_t2t_mou_7','loc_ic_t2t_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_ic_t2m_mou"]= tele[['loc_ic_t2m_mou_9','loc_ic_t2m_mou_7','loc_ic_t2m_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_ic_t2f_mou"]= tele[['loc_ic_t2f_mou_9','loc_ic_t2f_mou_7','loc_ic_t2f_mou_8']].apply(char_sum,axis=1)
tele["sum_loc_ic_mou"]= tele[['loc_ic_mou_9','loc_ic_mou_7','loc_ic_mou_8']].apply(char_sum,axis=1)
tele["sum_std_ic_t2t_mou"]= tele[['std_ic_t2t_mou_9','std_ic_t2t_mou_7','std_ic_t2t_mou_8']].apply(char_sum,axis=1)
tele["sum_std_ic_t2m_mou"]= tele[['std_ic_t2m_mou_9','std_ic_t2m_mou_7','std_ic_t2m_mou_8']].apply(char_sum,axis=1)
tele["sum_std_ic_t2f_mou"]= tele[['std_ic_t2f_mou_9','std_ic_t2f_mou_7','std_ic_t2f_mou_8']].apply(char_sum,axis=1)
tele["sum_std_ic_t2o_mou"]= tele[['std_ic_t2o_mou_9','std_ic_t2o_mou_7','std_ic_t2o_mou_8']].apply(char_sum,axis=1)
tele["sum_std_ic_mou"]= tele[['std_ic_mou_9','std_ic_mou_7','std_ic_mou_8']].apply(char_sum,axis=1)
tele["sum_total_ic_mou"]= tele[['total_ic_mou_9','total_ic_mou_7','total_ic_mou_8']].apply(char_sum,axis=1)
tele["sum_spl_ic_mou"]= tele[['spl_ic_mou_9','spl_ic_mou_7','spl_ic_mou_8']].apply(char_sum,axis=1)
tele["sum_isd_ic_mou"]= tele[['isd_ic_mou_9','isd_ic_mou_7','isd_ic_mou_8']].apply(char_sum,axis=1)
tele["sum_ic_others"]= tele[['ic_others_9','ic_others_7','ic_others_8']].apply(char_sum,axis=1)
tele["sum_total_rech_num"]= tele[['total_rech_num_9','total_rech_num_7','total_rech_num_8']].apply(char_sum,axis=1)
tele["sum_total_rech_amt"]= tele[['total_rech_amt_9','total_rech_amt_7','total_rech_amt_8']].apply(char_sum,axis=1)
tele["sum_max_rech_amt"]= tele[['max_rech_amt_9','max_rech_amt_7','max_rech_amt_8']].apply(char_sum,axis=1)
tele["sum_last_day_rch_amt"]= tele[['last_day_rch_amt_9','last_day_rch_amt_7','last_day_rch_amt_8']].apply(char_sum,axis=1)
tele["sum_total_rech_data"]= tele[['total_rech_data_9','total_rech_data_7','total_rech_data_8']].apply(char_sum,axis=1)
tele["sum_max_rech_data"]= tele[['max_rech_data_9','max_rech_data_7','max_rech_data_8']].apply(char_sum,axis=1)
tele["sum_count_rech_2g"]= tele[['count_rech_2g_9','count_rech_2g_7','count_rech_2g_8']].apply(char_sum,axis=1)
tele["sum_count_rech_3g"]= tele[['count_rech_3g_9','count_rech_3g_7','count_rech_3g_8']].apply(char_sum,axis=1)
tele["sum_av_rech_amt_data"]= tele[['av_rech_amt_data_9','av_rech_amt_data_7','av_rech_amt_data_8']].apply(char_sum,axis=1)
tele["sum_vol_2g_mb"]= tele[['vol_2g_mb_9','vol_2g_mb_7','vol_2g_mb_8']].apply(char_sum,axis=1)
tele["sum_vol_3g_mb"]= tele[['vol_3g_mb_9','vol_3g_mb_7','vol_3g_mb_8']].apply(char_sum,axis=1)
tele["sum_arpu_3g"]= tele[['arpu_3g_9','arpu_3g_7','arpu_3g_8']].apply(char_sum,axis=1)
tele["sum_arpu_2g"]= tele[['arpu_2g_9','arpu_2g_7','arpu_2g_8']].apply(char_sum,axis=1)
tele["sum_night_pck_user"]= tele[['night_pck_user_9','night_pck_user_7','night_pck_user_8']].apply(char_sum,axis=1)
tele["sum_av_rech_amt_data"]= tele[['av_rech_amt_data_9','av_rech_amt_data_7','av_rech_amt_data_8']].apply(char_sum,axis=1)
tele["sum_monthly_2g"]= tele[['monthly_2g_9','monthly_2g_7','monthly_2g_8']].apply(char_sum,axis=1)
tele["sum_sachet_2g"]= tele[['sachet_2g_9','sachet_2g_7','sachet_2g_8']].apply(char_sum,axis=1)
tele["sum_monthly_3g"]= tele[['monthly_3g_9','monthly_3g_7','monthly_3g_8']].apply(char_sum,axis=1)
tele["sum_sachet_3g"]= tele[['sachet_3g_9','sachet_3g_7','sachet_3g_8']].apply(char_sum,axis=1)
tele["sum_fb_user"]= tele[['fb_user_9','fb_user_7','fb_user_8']].apply(char_sum,axis=1)
tele["sum_vbc_3g"]= tele[['sep_vbc_3g','jul_vbc_3g','aug_vbc_3g']].apply(char_sum,axis=1)


tele["mean_arpu"]= tele[['arpu_9','arpu_7','arpu_8']].apply(char_mean,axis=1)
tele["mean_onnet_mou"]= tele[['onnet_mou_9','onnet_mou_7','onnet_mou_8']].apply(char_mean,axis=1)
tele["mean_ofnet_mou"]= tele[['offnet_mou_9','offnet_mou_7','offnet_mou_8']].apply(char_mean,axis=1)
tele["mean_roam_ic_mou"]= tele[['roam_ic_mou_9','roam_ic_mou_7','roam_ic_mou_8']].apply(char_mean,axis=1)
tele["mean_roam_og_mou"]= tele[['roam_og_mou_9','roam_og_mou_7','roam_og_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_og_t2t_mou"]= tele[['loc_og_t2t_mou_9','loc_og_t2t_mou_7','loc_og_t2t_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_og_t2m_mou"]= tele[['loc_og_t2m_mou_9','loc_og_t2m_mou_7','loc_og_t2m_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_og_t2f_mou"]= tele[['loc_og_t2f_mou_9','loc_og_t2f_mou_7','loc_og_t2f_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_og_t2c_mou"]= tele[['loc_og_t2c_mou_9','loc_og_t2c_mou_7','loc_og_t2c_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_og_mou"]= tele[['loc_og_mou_9','loc_og_mou_7','loc_og_mou_8']].apply(char_mean,axis=1)
tele["mean_std_og_t2t_mou"]= tele[['std_og_t2t_mou_9','std_og_t2t_mou_7','std_og_t2t_mou_8']].apply(char_mean,axis=1)
tele["mean_std_og_t2m_mou"]= tele[['std_og_t2m_mou_9','std_og_t2m_mou_7','std_og_t2m_mou_8']].apply(char_mean,axis=1)
tele["mean_std_og_t2f_mou"]= tele[['std_og_t2f_mou_9','std_og_t2f_mou_7','std_og_t2f_mou_8']].apply(char_mean,axis=1)
tele["mean_std_og_t2c_mou"]= tele[['std_og_t2c_mou_9','std_og_t2c_mou_7','std_og_t2c_mou_8']].apply(char_mean,axis=1)
tele["mean_isd_og_mou"]= tele[['isd_og_mou_9','isd_og_mou_7','isd_og_mou_8']].apply(char_mean,axis=1)
tele["mean_std_og_mou"]= tele[['std_og_mou_9','std_og_mou_7','std_og_mou_8']].apply(char_mean,axis=1)
tele["mean_spl_og_mou"]= tele[['spl_og_mou_9','spl_og_mou_7','spl_og_mou_8']].apply(char_mean,axis=1)
tele["mean_og_others"]= tele[['og_others_9','og_others_7','og_others_8']].apply(char_mean,axis=1)
tele["mean_total_og_mou"]= tele[['total_og_mou_9','total_og_mou_7','total_og_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_ic_t2t_mou"]= tele[['loc_ic_t2t_mou_9','loc_ic_t2t_mou_7','loc_ic_t2t_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_ic_t2m_mou"]= tele[['loc_ic_t2m_mou_9','loc_ic_t2m_mou_7','loc_ic_t2m_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_ic_t2f_mou"]= tele[['loc_ic_t2f_mou_9','loc_ic_t2f_mou_7','loc_ic_t2f_mou_8']].apply(char_mean,axis=1)
tele["mean_loc_ic_mou"]= tele[['loc_ic_mou_9','loc_ic_mou_7','loc_ic_mou_8']].apply(char_mean,axis=1)
tele["mean_std_ic_t2t_mou"]= tele[['std_ic_t2t_mou_9','std_ic_t2t_mou_7','std_ic_t2t_mou_8']].apply(char_mean,axis=1)
tele["mean_std_ic_t2m_mou"]= tele[['std_ic_t2m_mou_9','std_ic_t2m_mou_7','std_ic_t2m_mou_8']].apply(char_mean,axis=1)
tele["mean_std_ic_t2f_mou"]= tele[['std_ic_t2f_mou_9','std_ic_t2f_mou_7','std_ic_t2f_mou_8']].apply(char_mean,axis=1)
tele["mean_std_ic_t2o_mou"]= tele[['std_ic_t2o_mou_9','std_ic_t2o_mou_7','std_ic_t2o_mou_8']].apply(char_mean,axis=1)
tele["mean_std_ic_mou"]= tele[['std_ic_mou_9','std_ic_mou_7','std_ic_mou_8']].apply(char_mean,axis=1)
tele["mean_total_ic_mou"]= tele[['total_ic_mou_9','total_ic_mou_7','total_ic_mou_8']].apply(char_mean,axis=1)
tele["mean_spl_ic_mou"]= tele[['spl_ic_mou_9','spl_ic_mou_7','spl_ic_mou_8']].apply(char_mean,axis=1)
tele["mean_isd_ic_mou"]= tele[['isd_ic_mou_9','isd_ic_mou_7','isd_ic_mou_8']].apply(char_mean,axis=1)
tele["mean_ic_others"]= tele[['ic_others_9','ic_others_7','ic_others_8']].apply(char_mean,axis=1)
tele["mean_total_rech_num"]= tele[['total_rech_num_9','total_rech_num_7','total_rech_num_8']].apply(char_mean,axis=1)
tele["mean_total_rech_amt"]= tele[['total_rech_amt_9','total_rech_amt_7','total_rech_amt_8']].apply(char_mean,axis=1)
tele["mean_max_rech_amt"]= tele[['max_rech_amt_9','max_rech_amt_7','max_rech_amt_8']].apply(char_mean,axis=1)
tele["mean_last_day_rch_amt"]= tele[['last_day_rch_amt_9','last_day_rch_amt_7','last_day_rch_amt_8']].apply(char_mean,axis=1)
tele["mean_total_rech_data"]= tele[['total_rech_data_9','total_rech_data_7','total_rech_data_8']].apply(char_mean,axis=1)
tele["mean_max_rech_data"]= tele[['max_rech_data_9','max_rech_data_7','max_rech_data_8']].apply(char_mean,axis=1)
tele["mean_count_rech_2g"]= tele[['count_rech_2g_9','count_rech_2g_7','count_rech_2g_8']].apply(char_mean,axis=1)
tele["mean_count_rech_3g"]= tele[['count_rech_3g_9','count_rech_3g_7','count_rech_3g_8']].apply(char_mean,axis=1)
tele["mean_av_rech_amt_data"]= tele[['av_rech_amt_data_9','av_rech_amt_data_7','av_rech_amt_data_8']].apply(char_mean,axis=1)
tele["mean_vol_2g_mb"]= tele[['vol_2g_mb_9','vol_2g_mb_7','vol_2g_mb_8']].apply(char_mean,axis=1)
tele["mean_vol_3g_mb"]= tele[['vol_3g_mb_9','vol_3g_mb_7','vol_3g_mb_8']].apply(char_mean,axis=1)
tele["mean_arpu_3g"]= tele[['arpu_3g_9','arpu_3g_7','arpu_3g_8']].apply(char_mean,axis=1)
tele["mean_arpu_2g"]= tele[['arpu_2g_9','arpu_2g_7','arpu_2g_8']].apply(char_mean,axis=1)
tele["mean_night_pck_user"]= tele[['night_pck_user_9','night_pck_user_7','night_pck_user_8']].apply(char_mean,axis=1)
tele["mean_av_rech_amt_data"]= tele[['av_rech_amt_data_9','av_rech_amt_data_7','av_rech_amt_data_8']].apply(char_mean,axis=1)
tele["mean_monthly_2g"]= tele[['monthly_2g_9','monthly_2g_7','monthly_2g_8']].apply(char_mean,axis=1)
tele["mean_sachet_2g"]= tele[['sachet_2g_9','sachet_2g_7','sachet_2g_8']].apply(char_mean,axis=1)
tele["mean_monthly_3g"]= tele[['monthly_3g_9','monthly_3g_7','monthly_3g_8']].apply(char_mean,axis=1)
tele["mean_sachet_3g"]= tele[['sachet_3g_9','sachet_3g_7','sachet_3g_8']].apply(char_mean,axis=1)
tele["mean_fb_user"]= tele[['fb_user_9','fb_user_7','fb_user_8']].apply(char_mean,axis=1)
tele["mean_vbc_3g"]= tele[['sep_vbc_3g','jul_vbc_3g','aug_vbc_3g']].apply(char_mean,axis=1)


tele["mom_arpu"]= tele[['arpu_9','arpu_8']].apply(char_mom,axis=1)
tele["mom_onnet_mou"]= tele[['onnet_mou_9','onnet_mou_8']].apply(char_mom,axis=1)
tele["mom_ofnet_mou"]= tele[['offnet_mou_9','offnet_mou_8']].apply(char_mom,axis=1)
tele["mom_roam_ic_mou"]= tele[['roam_ic_mou_9','roam_ic_mou_8']].apply(char_mom,axis=1)
tele["mom_roam_og_mou"]= tele[['roam_og_mou_9','roam_og_mou_8']].apply(char_mom,axis=1)
tele["mom_loc_og_t2t_mou"]= tele[['loc_og_t2t_mou_9','loc_og_t2t_mou_8']].apply(char_mom,axis=1)
tele["mom_loc_og_t2m_mou"]= tele[['loc_og_t2m_mou_9','loc_og_t2m_mou_8']].apply(char_mom,axis=1)
tele["mom_loc_og_t2f_mou"]= tele[['loc_og_t2f_mou_9','loc_og_t2f_mou_8']].apply(char_mom,axis=1)
tele["mom_loc_og_t2c_mou"]= tele[['loc_og_t2c_mou_9','loc_og_t2c_mou_8']].apply(char_mom,axis=1)
tele["mom_loc_og_mou"]= tele[['loc_og_mou_9','loc_og_mou_8']].apply(char_mom,axis=1)
tele["mom_std_og_t2t_mou"]= tele[['std_og_t2t_mou_9','std_og_t2t_mou_8']].apply(char_mom,axis=1)
tele["mom_std_og_t2m_mou"]= tele[['std_og_t2m_mou_9','std_og_t2m_mou_8']].apply(char_mom,axis=1)
tele["mom_std_og_t2f_mou"]= tele[['std_og_t2f_mou_9','std_og_t2f_mou_8']].apply(char_mom,axis=1)
tele["mom_std_og_t2c_mou"]= tele[['std_og_t2c_mou_9','std_og_t2c_mou_8']].apply(char_mom,axis=1)
tele["mom_isd_og_mou"]= tele[['isd_og_mou_9','isd_og_mou_8']].apply(char_mom,axis=1)
tele["mom_std_og_mou"]= tele[['std_og_mou_9','std_og_mou_8']].apply(char_mom,axis=1)
tele["mom_spl_og_mou"]= tele[['spl_og_mou_9','spl_og_mou_8']].apply(char_mom,axis=1)
tele["mom_og_others"]= tele[['og_others_9','og_others_8']].apply(char_mom,axis=1)
tele["mom_total_og_mou"]= tele[['total_og_mou_9','total_og_mou_8']].apply(char_mom,axis=1)
tele["mom_loc_ic_t2t_mou"]= tele[['loc_ic_t2t_mou_9','loc_ic_t2t_mou_8']].apply(char_mom,axis=1)
tele["mom_loc_ic_t2m_mou"]= tele[['loc_ic_t2m_mou_9','loc_ic_t2m_mou_8']].apply(char_mom,axis=1)
tele["mom_loc_ic_t2f_mou"]= tele[['loc_ic_t2f_mou_9','loc_ic_t2f_mou_8']].apply(char_mom,axis=1)
tele["mom_loc_ic_mou"]= tele[['loc_ic_mou_9','loc_ic_mou_8']].apply(char_mom,axis=1)
tele["mom_std_ic_t2t_mou"]= tele[['std_ic_t2t_mou_9','std_ic_t2t_mou_8']].apply(char_mom,axis=1)
tele["mom_std_ic_t2m_mou"]= tele[['std_ic_t2m_mou_9','std_ic_t2m_mou_8']].apply(char_mom,axis=1)
tele["mom_std_ic_t2f_mou"]= tele[['std_ic_t2f_mou_9','std_ic_t2f_mou_8']].apply(char_mom,axis=1)
tele["mom_std_ic_t2o_mou"]= tele[['std_ic_t2o_mou_9','std_ic_t2o_mou_8']].apply(char_mom,axis=1)
tele["mom_std_ic_mou"]= tele[['std_ic_mou_9','std_ic_mou_8']].apply(char_mom,axis=1)
tele["mom_total_ic_mou"]= tele[['total_ic_mou_9','total_ic_mou_8']].apply(char_mom,axis=1)
tele["mom_spl_ic_mou"]= tele[['spl_ic_mou_9','spl_ic_mou_8']].apply(char_mom,axis=1)
tele["mom_isd_ic_mou"]= tele[['isd_ic_mou_9','isd_ic_mou_8']].apply(char_mom,axis=1)
tele["mom_ic_others"]= tele[['ic_others_9','ic_others_8']].apply(char_mom,axis=1)
tele["mom_total_rech_num"]= tele[['total_rech_num_9','total_rech_num_8']].apply(char_mom,axis=1)
tele["mom_total_rech_amt"]= tele[['total_rech_amt_9','total_rech_amt_8']].apply(char_mom,axis=1)
tele["mom_max_rech_amt"]= tele[['max_rech_amt_9','max_rech_amt_8']].apply(char_mom,axis=1)
tele["mom_last_day_rch_amt"]= tele[['last_day_rch_amt_9','last_day_rch_amt_8']].apply(char_mom,axis=1)
tele["mom_total_rech_data"]= tele[['total_rech_data_9','total_rech_data_8']].apply(char_mom,axis=1)
tele["mom_max_rech_data"]= tele[['max_rech_data_9','max_rech_data_8']].apply(char_mom,axis=1)
tele["mom_count_rech_2g"]= tele[['count_rech_2g_9','count_rech_2g_8']].apply(char_mom,axis=1)
tele["mom_count_rech_3g"]= tele[['count_rech_3g_9','count_rech_3g_8']].apply(char_mom,axis=1)
tele["mom_av_rech_amt_data"]= tele[['av_rech_amt_data_9','av_rech_amt_data_8']].apply(char_mom,axis=1)
tele["mom_vol_2g_mb"]= tele[['vol_2g_mb_9','vol_2g_mb_8']].apply(char_mom,axis=1)
tele["mom_vol_3g_mb"]= tele[['vol_3g_mb_9','vol_3g_mb_8']].apply(char_mom,axis=1)
tele["mom_arpu_3g"]= tele[['arpu_3g_9','arpu_3g_8']].apply(char_mom,axis=1)
tele["mom_arpu_2g"]= tele[['arpu_2g_9','arpu_2g_8']].apply(char_mom,axis=1)
tele["mom_night_pck_user"]= tele[['night_pck_user_9','night_pck_user_8']].apply(char_mom,axis=1)
tele["mom_av_rech_amt_data"]= tele[['av_rech_amt_data_9','av_rech_amt_data_8']].apply(char_mom,axis=1)
tele["mom_monthly_2g"]= tele[['monthly_2g_9','monthly_2g_8']].apply(char_mom,axis=1)
tele["mom_sachet_2g"]= tele[['sachet_2g_9','sachet_2g_8']].apply(char_mom,axis=1)
tele["mom_monthly_3g"]= tele[['monthly_3g_9','monthly_3g_8']].apply(char_mom,axis=1)
tele["mom_sachet_3g"]= tele[['sachet_3g_9','sachet_3g_8']].apply(char_mom,axis=1)
tele["mom_fb_user"]= tele[['fb_user_9','fb_user_8']].apply(char_mom,axis=1)
tele["mom_vbc_3g"]= tele[['sep_vbc_3g','aug_vbc_3g']].apply(char_mom,axis=1)

<ipython-input-14-5bbe2a8d61e6>:2: RuntimeWarning: divide by zero encountered in double_scalars
  MOM = x[0]/x[1]
<ipython-input-14-5bbe2a8d61e6>:2: RuntimeWarning: invalid value encountered in double_scalars
  MOM = x[0]/x[1]
<ipython-input-14-5bbe2a8d61e6>:2: RuntimeWarning: divide by zero encountered in longlong_scalars
  MOM = x[0]/x[1]
<ipython-input-14-5bbe2a8d61e6>:2: RuntimeWarning: invalid value encountered in longlong_scalars
  MOM = x[0]/x[1]


tele["moq_arpu"]= tele[['arpu_9','arpu_8','arpu_7']].apply(char_moq,axis=1)
tele["moq_onnet_mou"]= tele[['onnet_mou_9','onnet_mou_8','onnet_mou_7']].apply(char_moq,axis=1)
tele["moq_ofnet_mou"]= tele[['offnet_mou_9','offnet_mou_8','offnet_mou_7']].apply(char_moq,axis=1)
tele["moq_roam_ic_mou"]= tele[['roam_ic_mou_9','roam_ic_mou_8','roam_ic_mou_7']].apply(char_moq,axis=1)
tele["moq_roam_og_mou"]= tele[['roam_og_mou_9','roam_og_mou_8','roam_og_mou_7']].apply(char_moq,axis=1)
tele["moq_loc_og_t2t_mou"]= tele[['loc_og_t2t_mou_9','loc_og_t2t_mou_8','loc_og_t2t_mou_7']].apply(char_moq,axis=1)
tele["moq_loc_og_t2m_mou"]= tele[['loc_og_t2m_mou_9','loc_og_t2m_mou_8','loc_og_t2m_mou_7']].apply(char_moq,axis=1)
tele["moq_loc_og_t2f_mou"]= tele[['loc_og_t2f_mou_9','loc_og_t2f_mou_8','loc_og_t2f_mou_7']].apply(char_moq,axis=1)
tele["moq_loc_og_t2c_mou"]= tele[['loc_og_t2c_mou_9','loc_og_t2c_mou_8','loc_og_t2c_mou_7']].apply(char_moq,axis=1)
tele["moq_loc_og_mou"]= tele[['loc_og_mou_9','loc_og_mou_8','loc_og_mou_7']].apply(char_moq,axis=1)
tele["moq_std_og_t2t_mou"]= tele[['std_og_t2t_mou_9','std_og_t2t_mou_8','std_og_t2t_mou_7']].apply(char_moq,axis=1)
tele["moq_std_og_t2m_mou"]= tele[['std_og_t2m_mou_9','std_og_t2m_mou_8','std_og_t2m_mou_7']].apply(char_moq,axis=1)
tele["moq_std_og_t2f_mou"]= tele[['std_og_t2f_mou_9','std_og_t2f_mou_8','std_og_t2f_mou_7']].apply(char_moq,axis=1)
tele["moq_std_og_t2c_mou"]= tele[['std_og_t2c_mou_9','std_og_t2c_mou_8','std_og_t2c_mou_7']].apply(char_moq,axis=1)
tele["moq_isd_og_mou"]= tele[['isd_og_mou_9','isd_og_mou_8','isd_og_mou_7']].apply(char_moq,axis=1)
tele["moq_std_og_mou"]= tele[['std_og_mou_9','std_og_mou_8','std_og_mou_7']].apply(char_moq,axis=1)
tele["moq_spl_og_mou"]= tele[['spl_og_mou_9','spl_og_mou_8','spl_og_mou_7']].apply(char_moq,axis=1)
tele["moq_og_others"]= tele[['og_others_9','og_others_8','og_others_7']].apply(char_moq,axis=1)
tele["moq_total_og_mou"]= tele[['total_og_mou_9','total_og_mou_8','total_og_mou_7']].apply(char_moq,axis=1)
tele["moq_loc_ic_t2t_mou"]= tele[['loc_ic_t2t_mou_9','loc_ic_t2t_mou_8','loc_ic_t2t_mou_7']].apply(char_moq,axis=1)
tele["moq_loc_ic_t2m_mou"]= tele[['loc_ic_t2m_mou_9','loc_ic_t2m_mou_8','loc_ic_t2m_mou_7']].apply(char_moq,axis=1)
tele["moq_loc_ic_t2f_mou"]= tele[['loc_ic_t2f_mou_9','loc_ic_t2f_mou_8','loc_ic_t2f_mou_7']].apply(char_moq,axis=1)
tele["moq_loc_ic_mou"]= tele[['loc_ic_mou_9','loc_ic_mou_8','loc_ic_mou_7']].apply(char_moq,axis=1)
tele["moq_std_ic_t2t_mou"]= tele[['std_ic_t2t_mou_9','std_ic_t2t_mou_8','std_ic_t2t_mou_7']].apply(char_moq,axis=1)
tele["moq_std_ic_t2m_mou"]= tele[['std_ic_t2m_mou_9','std_ic_t2m_mou_8','std_ic_t2m_mou_7']].apply(char_moq,axis=1)
tele["moq_std_ic_t2f_mou"]= tele[['std_ic_t2f_mou_9','std_ic_t2f_mou_8','std_ic_t2f_mou_7']].apply(char_moq,axis=1)
tele["moq_std_ic_t2o_mou"]= tele[['std_ic_t2o_mou_9','std_ic_t2o_mou_8','std_ic_t2o_mou_7']].apply(char_moq,axis=1)
tele["moq_std_ic_mou"]= tele[['std_ic_mou_9','std_ic_mou_8','std_ic_mou_7']].apply(char_moq,axis=1)
tele["moq_total_ic_mou"]= tele[['total_ic_mou_9','total_ic_mou_8','total_ic_mou_7']].apply(char_moq,axis=1)
tele["moq_spl_ic_mou"]= tele[['spl_ic_mou_9','spl_ic_mou_8','spl_ic_mou_7']].apply(char_moq,axis=1)
tele["moq_isd_ic_mou"]= tele[['isd_ic_mou_9','isd_ic_mou_8','isd_ic_mou_7']].apply(char_moq,axis=1)
tele["moq_ic_others"]= tele[['ic_others_9','ic_others_8','ic_others_7']].apply(char_moq,axis=1)
tele["moq_total_rech_num"]= tele[['total_rech_num_9','total_rech_num_8','total_rech_num_7']].apply(char_moq,axis=1)
tele["moq_total_rech_amt"]= tele[['total_rech_amt_9','total_rech_amt_8','total_rech_amt_7']].apply(char_moq,axis=1)
tele["moq_max_rech_amt"]= tele[['max_rech_amt_9','max_rech_amt_8','max_rech_amt_7']].apply(char_moq,axis=1)
tele["moq_last_day_rch_amt"]= tele[['last_day_rch_amt_9','last_day_rch_amt_8','last_day_rch_amt_7']].apply(char_moq,axis=1)
tele["moq_total_rech_data"]= tele[['total_rech_data_9','total_rech_data_8','total_rech_data_7']].apply(char_moq,axis=1)
tele["moq_max_rech_data"]= tele[['max_rech_data_9','max_rech_data_8','max_rech_data_7']].apply(char_moq,axis=1)
tele["moq_count_rech_2g"]= tele[['count_rech_2g_9','count_rech_2g_8','count_rech_2g_7']].apply(char_moq,axis=1)
tele["moq_count_rech_3g"]= tele[['count_rech_3g_9','count_rech_3g_8','count_rech_3g_7']].apply(char_moq,axis=1)
tele["moq_av_rech_amt_data"]= tele[['av_rech_amt_data_9','av_rech_amt_data_8','av_rech_amt_data_7']].apply(char_moq,axis=1)
tele["moq_vol_2g_mb"]= tele[['vol_2g_mb_9','vol_2g_mb_8','vol_2g_mb_7']].apply(char_moq,axis=1)
tele["moq_vol_3g_mb"]= tele[['vol_3g_mb_9','vol_3g_mb_8','vol_3g_mb_7']].apply(char_moq,axis=1)
tele["moq_arpu_3g"]= tele[['arpu_3g_9','arpu_3g_8','arpu_3g_7']].apply(char_moq,axis=1)
tele["moq_arpu_2g"]= tele[['arpu_2g_9','arpu_2g_8','arpu_2g_7']].apply(char_moq,axis=1)
tele["moq_night_pck_user"]= tele[['night_pck_user_9','night_pck_user_8','night_pck_user_7']].apply(char_moq,axis=1)
tele["moq_av_rech_amt_data"]= tele[['av_rech_amt_data_9','av_rech_amt_data_8','av_rech_amt_data_7']].apply(char_moq,axis=1)
tele["moq_monthly_2g"]= tele[['monthly_2g_9','monthly_2g_8','monthly_2g_7']].apply(char_moq,axis=1)
tele["moq_sachet_2g"]= tele[['sachet_2g_9','sachet_2g_8','sachet_2g_7']].apply(char_moq,axis=1)
tele["moq_monthly_3g"]= tele[['monthly_3g_9','monthly_3g_8','monthly_3g_7']].apply(char_moq,axis=1)
tele["moq_sachet_3g"]= tele[['sachet_3g_9','sachet_3g_8','sachet_3g_7']].apply(char_moq,axis=1)
tele["moq_vbc_3g"]= tele[['sep_vbc_3g','aug_vbc_3g','jul_vbc_3g']].apply(char_moq,axis=1)

<ipython-input-16-1d2ffc61d510>:2: RuntimeWarning: invalid value encountered in double_scalars
  MOQ = x[0]/x[0]+x[1]+x[2]/3
<ipython-input-16-1d2ffc61d510>:2: RuntimeWarning: invalid value encountered in longlong_scalars
  MOQ = x[0]/x[0]+x[1]+x[2]/3


tele["arpu_inlast"]= tele['arpu_9']
tele["onnet_mou_inlast"]= tele['onnet_mou_9']
tele["ofnet_mou_inlast"]= tele['offnet_mou_9']
tele["roam_ic_mou_inlast"]= tele['roam_ic_mou_9']
tele["roam_og_mou_inlast"]= tele['roam_og_mou_9']
tele["loc_og_t2t_mou_inlast"]= tele['loc_og_t2t_mou_9']
tele["loc_og_t2m_mou_inlast"]= tele['loc_og_t2m_mou_9']
tele["loc_og_t2f_mou_inlast"]= tele['loc_og_t2f_mou_9']
tele["loc_og_t2c_mou_inlast"]= tele['loc_og_t2c_mou_9']
tele["loc_og_mou_inlast"]= tele['loc_og_mou_9']
tele["std_og_t2t_mou_inlast"]= tele['std_og_t2t_mou_9']
tele["std_og_t2m_mou_inlast"]= tele['std_og_t2m_mou_9']
tele["std_og_t2f_mou_inlast"]= tele['std_og_t2f_mou_9']
tele["std_og_t2c_mou_inlast"]= tele['std_og_t2c_mou_9']
tele["std_og_mou_inlast"]= tele['std_og_mou_9']
tele["isd_og_mou_inlast"]= tele['isd_og_mou_9']
tele["spl_og_mou_inlast"]= tele['spl_og_mou_9']
tele["og_others_inlast"]= tele['og_others_9']
tele["total_og_mou_inlast"]= tele['total_og_mou_9']
tele["loc_ic_t2t_mou_inlast"]= tele['loc_ic_t2t_mou_9']
tele["loc_ic_t2m_mou_inlast"]= tele['loc_ic_t2m_mou_9']
tele["loc_ic_t2f_mou_inlast"]= tele['loc_ic_t2f_mou_9']
tele["loc_ic_mou_inlast"]= tele['loc_ic_mou_9']
tele["std_ic_t2t_mou_inlast"]= tele['std_ic_t2t_mou_9']
tele["std_ic_t2m_mou_inlast"]= tele['std_ic_t2m_mou_9']
tele["std_ic_t2f_mou_inlast"]= tele['std_ic_t2f_mou_9']
tele["std_ic_t2o_mou_inlast"]= tele['std_ic_t2o_mou_9']
tele["std_ic_mou_inlast"]= tele['std_ic_mou_9']
tele["total_ic_mou_inlast"]= tele['total_ic_mou_9']
tele["spl_ic_mou_inlast"]= tele['spl_ic_mou_9']
tele["isd_ic_mou_inlast"]= tele['isd_ic_mou_9']
tele["ic_others_inlast"]= tele['ic_others_9']
tele["total_rech_num_inlast"]= tele['total_rech_num_9']
tele["total_rech_amt_inlast"]= tele['total_rech_amt_9']
tele["max_rech_amt_inlast"]= tele['max_rech_amt_9']
tele["last_day_rch_amt_inlast"]= tele['last_day_rch_amt_9']
tele["total_rech_data_inlast"]= tele['total_rech_data_9']
tele["max_rech_data_inlast"]= tele['max_rech_data_9']
tele["count_rech_2g_inlast"]= tele['count_rech_2g_9']
tele["count_rech_3g_inlast"]= tele['count_rech_3g_9']
tele["av_rech_amt_data_inlast"]= tele['av_rech_amt_data_9']
tele["vol_2g_mb_inlast"]= tele['vol_2g_mb_9']
tele["vol_3g_mb_inlast"]= tele['vol_3g_mb_9']
tele["arpu_3g_inlast"]= tele['arpu_3g_9']
tele["arpu_2g_inlast"]= tele['arpu_2g_9']
tele["night_pck_user_inlast"]= tele['night_pck_user_9']
tele["monthly_2g_inlast"]= tele['monthly_2g_9']
tele["sachet_2g_inlast"]= tele['sachet_2g_9']
tele["monthly_3g_inlast"]= tele['monthly_3g_9']
tele["sachet_3g_inlast"]= tele['sachet_3g_9']
tele["fb_user_inlast"]= tele['fb_user_9']
tele["aug_vbc_3g_inlast"]= tele['sep_vbc_3g']


tele["churn_flag"]=tele[["total_ic_mou_9","vol_3g_mb_9","vol_2g_mb_9","total_og_mou_9"]].apply(taeget,axis=1)


tele.shape

(99999, 591)


pd.set_option('display.max_columns',1000 )


tele1 =tele.drop(columns=['circle_id',
 'loc_og_t2o_mou',
 'std_og_t2o_mou',
 'loc_ic_t2o_mou','last_date_of_month_6',
 'last_date_of_month_7',
 'last_date_of_month_8',
 'last_date_of_month_9',
 'arpu_6',
 'arpu_7',
 'arpu_8',
 'arpu_9',
 'onnet_mou_6',
 'onnet_mou_7',
 'onnet_mou_8',
 'onnet_mou_9',
 'offnet_mou_6',
 'offnet_mou_7',
 'offnet_mou_8',
 'offnet_mou_9',
 'roam_ic_mou_6',
 'roam_ic_mou_7',
 'roam_ic_mou_8',
 'roam_ic_mou_9',
 'roam_og_mou_6',
 'roam_og_mou_7',
 'roam_og_mou_8',
 'roam_og_mou_9',
 'loc_og_t2t_mou_6',
 'loc_og_t2t_mou_7',
 'loc_og_t2t_mou_8',
 'loc_og_t2t_mou_9',
 'loc_og_t2m_mou_6',
 'loc_og_t2m_mou_7',
 'loc_og_t2m_mou_8',
 'loc_og_t2m_mou_9',
 'loc_og_t2f_mou_6',
 'loc_og_t2f_mou_7',
 'loc_og_t2f_mou_8',
 'loc_og_t2f_mou_9',
 'loc_og_t2c_mou_6',
 'loc_og_t2c_mou_7',
 'loc_og_t2c_mou_8',
 'loc_og_t2c_mou_9',
 'loc_og_mou_6',
 'loc_og_mou_7',
 'loc_og_mou_8',
 'loc_og_mou_9',
 'std_og_t2t_mou_6',
 'std_og_t2t_mou_7',
 'std_og_t2t_mou_8',
 'std_og_t2t_mou_9',
 'std_og_t2m_mou_6',
 'std_og_t2m_mou_7',
 'std_og_t2m_mou_8',
 'std_og_t2m_mou_9',
 'std_og_t2f_mou_6',
 'std_og_t2f_mou_7',
 'std_og_t2f_mou_8',
 'std_og_t2f_mou_9',
 'std_og_t2c_mou_6',
 'std_og_t2c_mou_7',
 'std_og_t2c_mou_8',
 'std_og_t2c_mou_9',
 'std_og_mou_6',
 'std_og_mou_7',
 'std_og_mou_8',
 'std_og_mou_9',
 'isd_og_mou_6',
 'isd_og_mou_7',
 'isd_og_mou_8',
 'isd_og_mou_9',
 'spl_og_mou_6',
 'spl_og_mou_7',
 'spl_og_mou_8',
 'spl_og_mou_9',
 'og_others_6',
 'og_others_7',
 'og_others_8',
 'og_others_9',
 'total_og_mou_6',
 'total_og_mou_7',
 'total_og_mou_8',
 'total_og_mou_9',
 'loc_ic_t2t_mou_6',
 'loc_ic_t2t_mou_7',
 'loc_ic_t2t_mou_8',
 'loc_ic_t2t_mou_9',
 'loc_ic_t2m_mou_6',
 'loc_ic_t2m_mou_7',
 'loc_ic_t2m_mou_8',
 'loc_ic_t2m_mou_9',
 'loc_ic_t2f_mou_6',
 'loc_ic_t2f_mou_7',
 'loc_ic_t2f_mou_8',
 'loc_ic_t2f_mou_9',
 'loc_ic_mou_6',
 'loc_ic_mou_7',
 'loc_ic_mou_8',
 'loc_ic_mou_9',
 'std_ic_t2t_mou_6',
 'std_ic_t2t_mou_7',
 'std_ic_t2t_mou_8',
 'std_ic_t2t_mou_9',
 'std_ic_t2m_mou_6',
 'std_ic_t2m_mou_7',
 'std_ic_t2m_mou_8',
 'std_ic_t2m_mou_9',
 'std_ic_t2f_mou_6',
 'std_ic_t2f_mou_7',
 'std_ic_t2f_mou_8',
 'std_ic_t2f_mou_9',
 'std_ic_t2o_mou_6',
 'std_ic_t2o_mou_7',
 'std_ic_t2o_mou_8',
 'std_ic_t2o_mou_9',
 'std_ic_mou_6',
 'std_ic_mou_7',
 'std_ic_mou_8',
 'std_ic_mou_9',
 'total_ic_mou_6',
 'total_ic_mou_7',
 'total_ic_mou_8',
 'total_ic_mou_9',
 'spl_ic_mou_6',
 'spl_ic_mou_7',
 'spl_ic_mou_8',
 'spl_ic_mou_9',
 'isd_ic_mou_6',
 'isd_ic_mou_7',
 'isd_ic_mou_8',
 'isd_ic_mou_9',
 'ic_others_6',
 'ic_others_7',
 'ic_others_8',
 'ic_others_9',
 'total_rech_num_6',
 'total_rech_num_7',
 'total_rech_num_8',
 'total_rech_num_9',
 'total_rech_amt_6',
 'total_rech_amt_7',
 'total_rech_amt_8',
 'total_rech_amt_9',
 'max_rech_amt_6',
 'max_rech_amt_7',
 'max_rech_amt_8',
 'max_rech_amt_9',
 'date_of_last_rech_6',
 'date_of_last_rech_7',
 'date_of_last_rech_8',
 'date_of_last_rech_9',
 'last_day_rch_amt_6',
 'last_day_rch_amt_7',
 'last_day_rch_amt_8',
 'last_day_rch_amt_9',
 'date_of_last_rech_data_6',
 'date_of_last_rech_data_7',
 'date_of_last_rech_data_8',
 'date_of_last_rech_data_9',
 'total_rech_data_6',
 'total_rech_data_7',
 'total_rech_data_8',
 'total_rech_data_9',
 'max_rech_data_6',
 'max_rech_data_7',
 'max_rech_data_8',
 'max_rech_data_9',
 'count_rech_2g_6',
 'count_rech_2g_7',
 'count_rech_2g_8',
 'count_rech_2g_9',
 'count_rech_3g_6',
 'count_rech_3g_7',
 'count_rech_3g_8',
 'count_rech_3g_9',
 'av_rech_amt_data_6',
 'av_rech_amt_data_7',
 'av_rech_amt_data_8',
 'av_rech_amt_data_9',
 'vol_2g_mb_6',
 'vol_2g_mb_7',
 'vol_2g_mb_8',
 'vol_2g_mb_9',
 'vol_3g_mb_6',
 'vol_3g_mb_7',
 'vol_3g_mb_8',
 'vol_3g_mb_9',
 'arpu_3g_6',
 'arpu_3g_7',
 'arpu_3g_8',
 'arpu_3g_9',
 'arpu_2g_6',
 'arpu_2g_7',
 'arpu_2g_8',
 'arpu_2g_9',
 'night_pck_user_6',
 'night_pck_user_7',
 'night_pck_user_8',
 'night_pck_user_9',
 'monthly_2g_6',
 'monthly_2g_7',
 'monthly_2g_8',
 'monthly_2g_9',
 'sachet_2g_6',
 'sachet_2g_7',
 'sachet_2g_8',
 'sachet_2g_9',
 'monthly_3g_6',
 'monthly_3g_7',
 'monthly_3g_8',
 'monthly_3g_9',
 'sachet_3g_6',
 'sachet_3g_7',
 'sachet_3g_8',
 'sachet_3g_9',
 'fb_user_6',
 'fb_user_7',
 'fb_user_8',
 'fb_user_9',
 'aon',
 'aug_vbc_3g',
 'jul_vbc_3g',
 'jun_vbc_3g',
 'sep_vbc_3g',
 'Usage'],axis=1)


tele1.shape

(99999, 365)


tele2 = tele1.dropna(thresh=75000,axis=1)


tele2.shape

(99999, 255)


def fill_na_num(data):
    for col in list(data.columns):
        data=data.fillna(data[col].mean())
        data=data.fillna(0)
        return data


tele_oot2 = fill_na_num(tele2)


list(tele_oot2.isnull().sum())

[0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0,
 0]


tele_oot2["churn_flag"]=pd.to_numeric(tele_oot2["churn_flag"],errors='coerce')


tele_score = tele_oot2.drop(['mobile_number'],axis=1)


Y_score=tele_score["churn_flag"]


Y_score.value_counts()

0    89808
1    10191
Name: churn_flag, dtype: int64


X_oot = tele_data.drop(['churn_flag'],axis=1)


pd.set_option("display.max_rows",1000)


X_oot.corr()


X_score=X_oot.drop(["sum_std_og_t2c_mou","sum_std_ic_t2o_mou","mom_arpu",
"mom_onnet_mou",
"mom_ofnet_mou",
"mom_loc_og_t2t_mou",
"mom_loc_og_t2m_mou",
"mom_loc_og_mou",
"mom_total_og_mou",
"mom_loc_ic_t2t_mou",
"mom_loc_ic_t2m_mou",
"mom_loc_ic_mou",
"mom_std_ic_mou",
"mom_total_ic_mou",
"mom_total_rech_num",
"mom_total_rech_amt",
"mom_max_rech_amt",
"mom_last_day_rch_amt","min_onnet_mou",
"min_ofnet_mou",
"min_roam_ic_mou",
"min_roam_og_mou",
"min_loc_og_t2t_mou",
"min_loc_og_t2m_mou",
"min_loc_og_t2f_mou",
"min_loc_og_t2c_mou",
"min_loc_og_mou",
"min_std_og_t2t_mou",
"min_std_og_t2m_mou",
"min_std_og_t2f_mou",
"min_std_og_t2c_mou",
"min_isd_og_mou",
"min_std_og_mou",
"min_spl_og_mou",
"min_og_others",
"min_loc_ic_t2t_mou",
"min_loc_ic_t2m_mou",
"min_loc_ic_t2f_mou",
"min_loc_ic_mou",
"min_std_ic_t2t_mou",
"min_std_ic_t2m_mou",
"min_std_ic_t2f_mou",
"min_std_ic_t2o_mou",
"min_std_ic_mou",
"min_spl_ic_mou",
"min_isd_ic_mou",
"min_ic_others",
"max_onnet_mou",
"max_ofnet_mou",
"max_roam_ic_mou",
"max_roam_og_mou",
"max_loc_og_t2t_mou",
"max_loc_og_t2m_mou",
"max_loc_og_t2f_mou",
"max_loc_og_t2c_mou",
"max_loc_og_mou",
"max_std_og_t2t_mou",
"max_std_og_t2m_mou",
"max_std_og_t2f_mou",
"max_std_og_t2c_mou",
"max_isd_og_mou",
"max_std_og_mou",
"max_spl_og_mou",
"max_og_others",
"max_loc_ic_t2t_mou",
"max_loc_ic_t2m_mou",
"max_loc_ic_t2f_mou",
"max_loc_ic_mou",
"max_std_ic_t2t_mou",
"max_std_ic_t2m_mou",
"max_std_ic_t2f_mou",
"max_std_ic_t2o_mou",
"max_std_ic_mou",
"max_spl_ic_mou",
"max_isd_ic_mou",
"max_ic_others","onnet_mou_inlast",
"ofnet_mou_inlast",
"roam_ic_mou_inlast",
"roam_og_mou_inlast",
"loc_og_t2t_mou_inlast",
"loc_og_t2m_mou_inlast",
"loc_og_t2f_mou_inlast",
"loc_og_t2c_mou_inlast",
"loc_og_mou_inlast",
"std_og_t2t_mou_inlast",
"std_og_t2m_mou_inlast",
"std_og_t2f_mou_inlast",
"std_og_t2c_mou_inlast",
"std_og_mou_inlast",
"isd_og_mou_inlast",
"spl_og_mou_inlast",
"og_others_inlast",
"loc_ic_t2t_mou_inlast",
"loc_ic_t2m_mou_inlast",
"loc_ic_t2f_mou_inlast",
"loc_ic_mou_inlast",
"std_ic_t2t_mou_inlast",
"std_ic_t2m_mou_inlast",
"std_ic_t2f_mou_inlast",
"std_ic_t2o_mou_inlast",
"std_ic_mou_inlast",
"spl_ic_mou_inlast",
"isd_ic_mou_inlast",
"ic_others_inlast","sum_arpu",
"sum_total_rech_num",
"sum_vol_3g_mb",
"sum_sachet_2g",
"sum_monthly_3g",
"sum_vbc_3g",
"mean_onnet_mou",
"mean_ofnet_mou",
"mean_roam_ic_mou",
"mean_roam_og_mou",
"mean_loc_og_t2t_mou",
"mean_loc_og_t2m_mou",
"mean_loc_og_t2f_mou",
"mean_loc_og_t2c_mou",
"mean_loc_og_mou",
"mean_std_og_t2t_mou",
"mean_std_og_t2m_mou",
"mean_std_og_t2f_mou",
"mean_std_og_t2c_mou",
"mean_isd_og_mou",
"mean_std_og_mou",
"mean_spl_og_mou",
"mean_og_others",
"mean_loc_ic_t2t_mou",
"mean_loc_ic_t2m_mou",
"mean_loc_ic_t2f_mou",
"mean_loc_ic_mou",
"mean_std_ic_t2t_mou",
"mean_std_ic_t2m_mou",
"mean_std_ic_t2f_mou",
"mean_std_ic_t2o_mou",
"mean_std_ic_mou",
"mean_spl_ic_mou",
"mean_isd_ic_mou",
"mean_ic_others",
 "mean_vol_3g_mb",
"mean_sachet_2g",
"mean_monthly_3g",
"mean_vbc_3g","sum_total_og_mou",
"sum_total_ic_mou",
"sum_total_rech_amt",
"sum_max_rech_amt",
"sum_last_day_rch_amt",
"sum_vol_2g_mb",
"sum_monthly_2g",
"sum_sachet_3g",
"moq_total_rech_amt","max_arpu","min_total_rech_amt"
],axis=1)


X_score.shape

(99999, 99)


## LG
logistic_churn_tele = pickle.load(open("logistic_churn.sav", 'rb'))


def get_predictions(X_score,model):
    y_pred_df = pd.DataFrame({'actual':Y_score,
                             'predicted_prob_LG':logistic_churn_tele.predict(sm.add_constant(X_score))})
    return y_pred_df


y_pred_oot = get_predictions(X_score,lg)


y_pred_oot.head()


y_pred_oot["pred_class_oot"]= y_pred_oot.predicted_prob_LG.map(lambda X:1 if X>0.5 else 0)


model_predictions_LG = y_pred_oot.drop(["pred_class_oot"],axis=1)


model_predictions_LG.head()

#DT


Decision_churn_tele = pickle.load(open("Decision_Tree_churn.sav", 'rb'))


check = Decision_churn_tele.predict(X_score)


check

array([0, 0, 0, ..., 1, 0, 1], dtype=int64)


model_predictions1_oot = pd.DataFrame({'Actual':Y_score, 'predictions':check})


model_predictions1_oot.head()


y_pred_prob = Decision_churn_tele.predict_proba(X_score)


y_pred_prob = Decision_churn_tele.predict_proba(X_score)[:, 1]


model_predictions1_oot['Predicted_prob_DT'] = y_pred_prob


model_predictions_DT=model_predictions1_oot.drop(["Actual","predictions"],axis=1)


model_predictions_DT.head()


Random_churn_tele = pickle.load(open("Random_Forest_churn.sav", 'rb'))


check1 = Random_churn_tele.predict(X_score)


check1

array([0, 0, 0, ..., 1, 0, 1], dtype=int64)


model_predictions1_R = pd.DataFrame({'Actual':Y_score, 'predictions':check1})


model_predictions1_R.head()


y_pred_prob2 = Random_churn_tele.predict_proba(X_score)


y_pred_prob2 = Random_churn_tele.predict_proba(X_score)[:, 1]


model_predictions1_R['Predicted_prob_RF'] = y_pred_prob2


model_predictions_RF=model_predictions1_R.drop(["Actual","predictions"],axis=1)


model_predictions_RF.head()


GB_churn_tele = pickle.load(open("Gradient_Boosting_churn.sav", 'rb'))


check2 = GB_churn_tele.predict(X_score)


check2

array([0, 0, 0, ..., 1, 0, 1], dtype=int64)


model_predictions1_GB = pd.DataFrame({'Actual':Y_score, 'predictions':check2})


model_predictions1_GB.head()


y_pred_prob1 = GB_churn_tele.predict_proba(X_score)


y_pred_prob1= GB_churn_tele.predict_proba(X_score)[:, 1]


model_predictions1_GB['Predicted_prob_GB'] = y_pred_prob1


model_predictions1_GB.head()


model_predictions_GB=model_predictions1_GB.drop(["Actual","predictions"],axis=1)


model_predictions_GB.head()


final_data = pd.concat([model_predictions_LG,model_predictions_DT,model_predictions_RF,model_predictions_GB],axis=1)


final_data


final_data["Avg_prob"]=final_data[["predicted_prob_LG","Predicted_prob_DT","Predicted_prob_RF","Predicted_prob_GB"]].mean(axis=1)


final_data


final_avg = final_data.drop(["predicted_prob_LG","Predicted_prob_DT","Predicted_prob_RF","Predicted_prob_GB"],axis=1)


final_avg


final_avg['decile'] = pd.qcut(final_avg['Avg_prob'],10,labels=['1','2','3','4','5','6','7','8','9','10'])
final_avg.head()


final_avg.columns = ['Defaulter','Probability','Decile']
final_avg.head()


final_avg['Non-Defaulter'] = 1-final_avg['Defaulter']
final_avg.head()


final_data = pd.pivot_table(data=final_avg,index=['Decile'],values=['Defaulter','Non-Defaulter','Probability'],
                     aggfunc={'Defaulter':[np.sum],
                              'Non-Defaulter':[np.sum],
                              'Probability' : [np.min,np.max]})
final_data


final_data.reset_index()


final_data.columns = ['Defaulter_Count','Non-Defaulter_Count','max_score','min_score']
final_data['Total_Cust'] = final_data['Defaulter_Count']+final_data['Non-Defaulter_Count']
final_data


final = final_data.sort_values(by='min_score',ascending=False)
final


final['Default_Rate'] = (final['Defaulter_Count'] / final['Total_Cust']).apply('{0:.2%}'.format)
default_sum = final['Defaulter_Count'].sum()
non_default_sum = final['Non-Defaulter_Count'].sum()
final['Default %'] = (final['Defaulter_Count']/default_sum).apply('{0:.2%}'.format)
final['Non_Default %'] = (final['Non-Defaulter_Count']/non_default_sum).apply('{0:.2%}'.format)
final


final['ks_stats'] = np.round(((final['Defaulter_Count'] / final['Defaulter_Count'].sum()).cumsum() -(final['Non-Defaulter_Count'] / final['Non-Defaulter_Count'].sum()).cumsum()), 4) * 100
final


flag = lambda x: '*****' if x == final['ks_stats'].max() else ''
final['max_ks'] = final['ks_stats'].apply(flag)
final


final['default_cum%'] = np.round(((final['Defaulter_Count'] / final['Defaulter_Count'].sum()).cumsum()), 4) * 100
final


final["Risk_class"]=np.where(final["min_score"] >=0.045, "High_Risk",
                                np.where(((final["min_score"]<=0.045) & (final["min_score"]>0.029)) , "Medium_Risk", "Low_Risk"))


final

	mobile_number	circle_id	last_date_of_month_6	last_date_of_month_7	last_date_of_month_8	last_date_of_month_9	arpu_6	...	moq_vol_2g_mb	moq_vol_3g_mb	moq_arpu_3g	moq_arpu_2g	moq_night_pck_user	moq_monthly_2g	moq_sachet_2g	moq_monthly_3g	moq_sachet_3g	moq_vbc_3g
0	7000842753	109	6/30/2014	7/31/2014	8/31/2014	9/30/2014	197.385	...	12.363333	179.616667	283.893333	283.893333	NaN	NaN	NaN	2.333333	NaN	34.733333
1	7001865778	109	6/30/2014	7/31/2014	8/31/2014	9/30/2014	34.047	...	109.070000	NaN	NaN	NaN	NaN	NaN	1.0	NaN	NaN	NaN
2	7001625959	109	6/30/2014	7/31/2014	8/31/2014	9/30/2014	167.690	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	7001204172	109	6/30/2014	7/31/2014	8/31/2014	9/30/2014	221.338	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	7000142493	109	6/30/2014	7/31/2014	8/31/2014	9/30/2014	261.636	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	mobile_number	min_arpu	min_onnet_mou	min_ofnet_mou	min_loc_og_t2t_mou	min_loc_og_t2m_mou	min_loc_og_t2f_mou	min_loc_og_mou	min_std_og_t2t_mou	min_std_og_t2m_mou	min_std_og_t2f_mou	min_std_og_mou	min_spl_og_mou	min_total_og_mou	min_loc_ic_t2t_mou	min_loc_ic_t2m_mou	min_loc_ic_t2f_mou	min_loc_ic_mou	min_std_ic_t2t_mou	min_std_ic_t2m_mou	min_std_ic_t2f_mou	min_std_ic_mou	min_total_ic_mou	min_total_rech_num	min_total_rech_amt	min_max_rech_amt	min_last_day_rch_amt	min_total_rech_data	min_max_rech_data	min_count_rech_2g	min_count_rech_3g	min_av_rech_amt_data	min_vol_2g_mb	min_vol_3g_mb	min_arpu_3g	min_arpu_2g	min_night_pck_user	min_monthly_3g	min_fb_user	max_arpu	max_onnet_mou	max_ofnet_mou	max_roam_ic_mou	max_roam_og_mou	max_loc_og_t2t_mou	max_loc_og_t2m_mou	max_loc_og_t2f_mou	max_loc_og_t2c_mou	max_loc_og_mou	max_std_og_t2t_mou	max_std_og_t2m_mou	max_std_og_t2f_mou	max_std_og_mou	max_spl_og_mou	max_og_others	max_total_og_mou	max_loc_ic_t2t_mou	max_loc_ic_t2m_mou	max_loc_ic_t2f_mou	max_loc_ic_mou	max_std_ic_t2t_mou	max_std_ic_t2m_mou	max_std_ic_t2f_mou	max_std_ic_mou	max_total_ic_mou	max_spl_ic_mou	max_isd_ic_mou	max_ic_others	max_total_rech_num	max_total_rech_amt	max_max_rech_amt	max_last_day_rch_amt	max_total_rech_data	max_max_rech_data	max_count_rech_2g	max_count_rech_3g	max_av_rech_amt_data	max_vol_2g_mb	max_vol_3g_mb	max_arpu_3g	max_arpu_2g	max_night_pck_user	max_monthly_2g	max_sachet_2g	max_monthly_3g	max_fb_user	max_vbc_3g	sum_arpu	sum_onnet_mou	sum_ofnet_mou	sum_roam_ic_mou	sum_roam_og_mou	sum_loc_og_t2t_mou	sum_loc_og_t2m_mou	sum_loc_og_t2f_mou	sum_loc_og_t2c_mou	sum_loc_og_mou	sum_std_og_t2t_mou	sum_std_og_t2m_mou	sum_std_og_t2f_mou	sum_std_og_mou	sum_spl_og_mou	sum_og_others	sum_total_og_mou	sum_loc_ic_t2t_mou	sum_loc_ic_t2m_mou	sum_loc_ic_t2f_mou	sum_loc_ic_mou	sum_std_ic_t2t_mou	sum_std_ic_t2m_mou	sum_std_ic_t2f_mou	sum_std_ic_mou	sum_total_ic_mou	sum_spl_ic_mou	sum_isd_ic_mou	sum_ic_others	sum_total_rech_num	sum_total_rech_amt	sum_max_rech_amt	sum_last_day_rch_amt	sum_total_rech_data	sum_max_rech_data	sum_count_rech_2g	sum_count_rech_3g	sum_av_rech_amt_data	sum_vol_2g_mb	sum_vol_3g_mb	sum_arpu_3g	sum_arpu_2g	sum_monthly_2g	sum_sachet_2g	sum_monthly_3g	sum_fb_user	sum_vbc_3g	mean_arpu	mean_onnet_mou	mean_ofnet_mou	mean_roam_ic_mou	mean_roam_og_mou	mean_loc_og_t2t_mou	mean_loc_og_t2m_mou	mean_loc_og_t2f_mou	mean_loc_og_t2c_mou	mean_loc_og_mou	mean_std_og_t2t_mou	mean_std_og_t2m_mou	mean_std_og_t2f_mou	mean_std_og_mou	mean_spl_og_mou	mean_og_others	mean_total_og_mou	mean_loc_ic_t2t_mou	mean_loc_ic_t2m_mou	mean_loc_ic_t2f_mou	mean_loc_ic_mou	mean_std_ic_t2t_mou	mean_std_ic_t2m_mou	mean_std_ic_t2f_mou	mean_std_ic_mou	mean_total_ic_mou	mean_spl_ic_mou	mean_isd_ic_mou	mean_ic_others	mean_total_rech_num	mean_total_rech_amt	mean_max_rech_amt	mean_last_day_rch_amt	mean_total_rech_data	mean_max_rech_data	mean_count_rech_2g	mean_count_rech_3g	mean_av_rech_amt_data	mean_vol_2g_mb	mean_vol_3g_mb	mean_arpu_3g	mean_arpu_2g	mean_night_pck_user	mean_monthly_2g	mean_sachet_2g	mean_monthly_3g	mean_fb_user	mean_vbc_3g	mom_arpu	mom_onnet_mou	mom_ofnet_mou	mom_roam_ic_mou	mom_roam_og_mou	mom_loc_og_t2t_mou	mom_loc_og_t2m_mou	mom_loc_og_t2f_mou	mom_loc_og_t2c_mou	mom_loc_og_mou	mom_std_og_t2t_mou	mom_std_og_t2m_mou	mom_std_og_t2f_mou	mom_std_og_t2c_mou	mom_isd_og_mou	mom_std_og_mou	mom_spl_og_mou	mom_og_others	mom_total_og_mou	mom_loc_ic_t2t_mou	mom_loc_ic_t2m_mou	mom_loc_ic_t2f_mou	mom_loc_ic_mou	mom_std_ic_t2t_mou	mom_std_ic_t2m_mou	mom_std_ic_t2f_mou	mom_std_ic_t2o_mou	mom_std_ic_mou	mom_total_ic_mou	mom_spl_ic_mou	mom_isd_ic_mou	mom_ic_others	mom_total_rech_num	mom_total_rech_amt	mom_max_rech_amt	mom_last_day_rch_amt	mom_total_rech_data	mom_max_rech_data	mom_count_rech_2g	mom_count_rech_3g	mom_av_rech_amt_data	mom_vol_2g_mb	mom_vol_3g_mb	mom_arpu_3g	mom_arpu_2g	mom_night_pck_user	mom_monthly_2g	mom_sachet_2g	mom_monthly_3g	mom_sachet_3g	mom_fb_user	mom_vbc_3g	moq_arpu	moq_onnet_mou	moq_ofnet_mou	moq_roam_ic_mou	moq_roam_og_mou	moq_loc_og_t2t_mou	moq_loc_og_t2m_mou	moq_loc_og_t2f_mou	moq_loc_og_t2c_mou	moq_loc_og_mou	moq_std_og_t2t_mou	moq_std_og_t2m_mou	moq_std_og_t2f_mou	moq_std_og_t2c_mou	moq_isd_og_mou	moq_std_og_mou	moq_spl_og_mou	moq_og_others	moq_total_og_mou	moq_loc_ic_t2t_mou	moq_loc_ic_t2m_mou	moq_loc_ic_t2f_mou	moq_loc_ic_mou	moq_std_ic_t2t_mou	moq_std_ic_t2m_mou	moq_std_ic_t2f_mou	moq_std_ic_t2o_mou	moq_std_ic_mou	moq_total_ic_mou	moq_spl_ic_mou	moq_isd_ic_mou	moq_ic_others	moq_total_rech_num	moq_total_rech_amt	moq_max_rech_amt	moq_last_day_rch_amt	moq_total_rech_data	moq_max_rech_data	moq_count_rech_2g	moq_count_rech_3g	moq_av_rech_amt_data	moq_vol_2g_mb	moq_vol_3g_mb	moq_arpu_3g	moq_arpu_2g	moq_night_pck_user	moq_monthly_2g	moq_sachet_2g	moq_monthly_3g	moq_sachet_3g	moq_vbc_3g	arpu_inlast	onnet_mou_inlast	ofnet_mou_inlast	roam_ic_mou_inlast	roam_og_mou_inlast	loc_og_t2t_mou_inlast	loc_og_t2m_mou_inlast	loc_og_t2f_mou_inlast	loc_og_t2c_mou_inlast	loc_og_mou_inlast	std_og_t2t_mou_inlast	std_og_t2m_mou_inlast	std_og_t2f_mou_inlast	std_og_mou_inlast	spl_og_mou_inlast	total_og_mou_inlast	loc_ic_t2t_mou_inlast	loc_ic_t2m_mou_inlast	loc_ic_t2f_mou_inlast	loc_ic_mou_inlast	std_ic_t2t_mou_inlast	std_ic_t2m_mou_inlast	std_ic_t2f_mou_inlast	std_ic_mou_inlast	total_ic_mou_inlast	total_rech_num_inlast	total_rech_amt_inlast	max_rech_amt_inlast	last_day_rch_amt_inlast	total_rech_data_inlast	max_rech_data_inlast	count_rech_2g_inlast	count_rech_3g_inlast	av_rech_amt_data_inlast	vol_2g_mb_inlast	vol_3g_mb_inlast	arpu_3g_inlast	arpu_2g_inlast	night_pck_user_inlast	sachet_2g_inlast	monthly_3g_inlast	fb_user_inlast	aug_vbc_3g_inlast	churn_flag
0	7000842753	197.385	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.16	4.13	1.15	5.44	0.00	0.00	0.00	0.00	0.00	2	252	252	252	1.0	252.0	0.0	1.0	252.0	1.32	83.57	212.17	212.17	0.0	1	1.0	214.816	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.16	4.13	1.15	5.44	0.00	0.00	0.00	0.00	5.44	0.00	0.00	0.00	4	362	252	252	1.0	252.0	0.0	1.0	252.0	30.13	150.76	212.17	212.17	0.0	0	0	1	1.0	101.20	626.004	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.16	4.13	1.15	5.44	0.00	0.00	0.00	0.00	5.44	0.00	0.00	0.00	9	866	756	756	3.0	756.0	0.0	3.0	756.0	37.20	343.94	636.51	636.51	0	0	3	3.0	131.60	208.668000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.160000	4.130000	1.150000	5.440000	0.000000	0.000000	0.000000	0.000000	1.813333	0.000000	0.000000	0.000000	3.000000	288.666667	252.000000	252.000000	1.0	252.0	0.0	1.0	252.0	12.400000	114.646667	212.17	212.170	0.0	0.000000	0.000000	1.0	1.0	43.866667	0.995284	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	inf	NaN	NaN	NaN	0.666667	1.000000	1.000000	1.000000	1.0	1.000000	NaN	1.0	1.000000	4.356061	0.72705	1.0	1.000000	NaN	NaN	NaN	1.0	NaN	1.0	inf	281.611000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.000000	NaN	NaN	NaN	5.333333	373.666667	337.000000	337.000000	2.333333	337.0	NaN	2.333333	337.0	12.363333	179.616667	283.893333	283.893333	NaN	NaN	NaN	2.333333	NaN	34.733333	213.803	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.16	4.13	1.15	5.44	0.00	0.00	0.00	0.00	5.44	2	252	252	252	1.0	252.0	0.0	1.0	252.0	5.75	109.61	212.17	212.17	0.0	0	1	1.0	30.4	1
1	7001865778	34.047	7.68	15.74	7.68	11.51	0.00	35.39	0.00	0.00	0.00	0.13	4.68	40.31	1.61	17.48	0.00	19.09	0.00	0.00	0.00	0.00	26.83	4	74	44	23	1.0	25.0	1.0	0.0	50.0	0.00	0.00	0.00	7.60	0.0	0	1.0	355.074	78.68	304.76	0.00	0.00	74.56	291.86	0.00	2.91	299.54	4.11	0.46	0.00	4.58	23.43	0.00	312.44	29.91	375.58	8.93	408.43	2.35	12.49	0.00	14.84	423.28	0.00	1.83	0.00	11	384	154	44	2.0	154.0	2.0	0.0	154.0	365.47	0.00	0.00	28.61	0.0	1	2	0	1.0	0.00	657.442	110.47	420.34	0.00	0.00	106.12	379.31	0.00	2.91	485.44	4.34	0.59	0.00	4.94	40.87	0.00	531.28	60.75	458.44	12.54	531.75	2.35	18.39	0.00	20.74	554.34	0.00	1.83	0.00	24	741	263	97	3.0	179.0	3.0	0.0	204.0	473.54	0.00	0.00	36.21	1	2	0	2.0	0.00	219.147333	36.823333	140.113333	0.00	0.000000	35.373333	126.436667	0.000000	0.970000	161.813333	1.446667	0.196667	0.000000	1.646667	13.623333	0.00	177.093333	20.250000	152.813333	4.180000	177.250000	0.783333	6.130000	0.000000	6.913333	184.780000	0.000000	0.610000	0.000000	8.000000	247.000000	87.666667	32.333333	1.5	89.5	1.5	0.0	102.0	157.846667	0.000000	0.00	18.105	0.0	0.333333	0.666667	0.0	1.0	0.000000	0.755676	0.097611	3.052484	NaN	NaN	0.103004	3.843297	NaN	0.000000	1.990167	0.000000	0.282609	NaN	NaN	NaN	0.028384	0.544601	NaN	1.750070	0.977265	5.744570	0.404255	3.918546	inf	inf	NaN	NaN	inf	4.061019	NaN	NaN	NaN	1.222222	0.736979	0.422078	1.304348	2.0	0.162338	2.0	NaN	0.324675	3.381790	NaN	NaN	0.265641	NaN	0.0	inf	NaN	NaN	1.0	NaN	367.423000	87.716667	106.086667	NaN	NaN	83.520000	80.776667	NaN	NaN	163.306667	NaN	1.460000	NaN	NaN	NaN	5.656667	25.990000	NaN	192.966667	31.446667	72.206667	9.930000	111.593333	1.000000	2.966667	NaN	NaN	2.966667	114.173333	NaN	NaN	NaN	11.333333	409.666667	169.666667	38.666667	NaN	NaN	NaN	NaN	NaN	109.070000	NaN	NaN	NaN	NaN	NaN	1.0	NaN	NaN	NaN	268.321	7.68	304.76	0.00	0.00	7.68	291.86	0.00	0.00	299.54	0.00	0.13	0.00	0.13	12.76	312.44	29.23	375.58	3.61	408.43	2.35	12.49	0.00	14.84	423.28	11	283	65	30	2.0	25.0	2.0	0.0	50.0	365.47	0.00	0.00	7.60	0.0	2	0	1.0	0.0	0
2	7001625959	167.690	11.54	143.33	7.19	16.86	15.61	60.66	4.34	41.81	1.48	47.64	46.56	155.33	67.46	14.38	49.63	155.99	28.69	45.18	21.73	139.33	370.04	2	116	86	0	NaN	NaN	NaN	NaN	NaN	0.00	0.00	NaN	NaN	NaN	0	NaN	210.226	55.24	220.59	0.00	0.00	28.74	38.46	24.11	135.54	67.66	26.49	75.53	22.83	120.94	236.84	0.45	412.94	115.69	38.89	122.29	229.56	72.41	177.01	58.34	306.66	519.53	0.21	0.85	3.14	5	315	200	200	NaN	NaN	NaN	NaN	NaN	0.00	0.00	NaN	NaN	NaN	0	0	0	NaN	4.17	566.974	104.04	572.28	0.00	0.00	49.51	84.66	61.51	181.30	195.73	53.41	184.75	39.07	277.26	380.24	0.45	853.73	254.26	68.71	271.40	594.41	172.39	389.28	123.30	685.02	1284.60	0.21	0.85	4.07	11	599	372	286	0.0	0.0	0.0	0.0	0.0	0.00	0.00	0.00	0.00	0	0	0	0.0	4.17	188.991333	34.680000	190.760000	0.00	0.000000	16.503333	28.220000	20.503333	60.433333	65.243333	17.803333	61.583333	13.023333	92.420000	126.746667	0.15	284.576667	84.753333	22.903333	90.466667	198.136667	57.463333	129.760000	41.100000	228.340000	428.200000	0.070000	0.283333	1.356667	3.666667	199.666667	124.000000	95.333333	NaN	NaN	NaN	NaN	NaN	0.000000	0.000000	NaN	NaN	NaN	0.000000	0.000000	0.0	NaN	1.390000	1.111966	0.674511	0.944558	NaN	NaN	0.472512	2.281139	0.716384	0.337613	1.003709	0.852397	1.120457	1.546748	NaN	NaN	1.112808	0.408884	NaN	0.691287	0.948671	2.518782	0.405839	0.746864	0.402441	0.943958	0.741001	NaN	0.779463	0.760360	NaN	0.0	0.0	0.500000	0.368254	0.430000	0.430000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	245.954667	60.086667	269.366667	NaN	NaN	32.136667	27.640000	30.826667	136.54	88.630000	28.936667	82.346667	16.253333	NaN	NaN	125.560000	253.360000	NaN	465.716667	110.673333	21.233333	156.450000	286.380000	96.426667	193.070000	66.583333	NaN	354.103333	643.876667	NaN	NaN	NaN	6.666667	372.000000	229.666667	201.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	210.226	37.26	208.36	0.00	0.00	13.58	38.46	15.61	45.76	67.66	22.58	75.53	22.83	120.94	96.84	285.46	67.46	38.89	49.63	155.99	28.69	167.09	43.23	239.03	395.03	2	116	86	86	NaN	NaN	NaN	NaN	NaN	0.00	0.00	NaN	NaN	NaN	0	0	NaN	0.0	0
3	7001204172	221.338	54.39	71.68	10.61	22.46	0.65	37.99	14.89	1.73	0.00	16.63	0.00	135.31	8.04	20.28	19.84	48.18	0.00	1.33	0.00	4.94	53.13	10	230	50	30	NaN	NaN	NaN	NaN	NaN	0.00	0.00	NaN	NaN	NaN	0	NaN	508.054	310.98	123.31	54.86	39.04	73.68	107.43	4.91	0.00	183.03	289.58	6.53	0.00	296.11	18.09	0.00	352.21	62.08	113.96	57.43	233.48	66.44	38.56	1.18	105.01	280.08	0.59	0.00	0.00	18	601	60	50	NaN	NaN	NaN	NaN	NaN	0.00	0.00	NaN	NaN	NaN	0	0	0	NaN	0.00	980.494	465.28	304.00	99.24	67.13	119.10	213.10	7.47	0.00	339.70	330.70	11.25	0.00	341.97	29.05	0.00	710.75	90.10	198.75	104.36	393.25	109.92	44.83	1.18	155.94	549.82	0.59	0.00	0.00	39	1141	160	130	0.0	0.0	0.0	0.0	0.0	0.00	0.00	0.00	0.00	0	0	0	0.0	0.00	326.831333	155.093333	101.333333	33.08	22.376667	39.700000	71.033333	2.490000	0.000000	113.233333	110.233333	3.750000	0.000000	113.990000	9.683333	0.00	236.916667	30.033333	66.250000	34.786667	131.083333	36.640000	14.943333	0.393333	51.980000	183.273333	0.196667	0.000000	0.000000	13.000000	380.333333	53.333333	43.333333	NaN	NaN	NaN	NaN	NaN	0.000000	0.000000	NaN	NaN	NaN	0.000000	0.000000	0.0	NaN	0.000000	2.023297	5.717595	0.657554	0.808968	1.389818	0.304797	0.269919	7.553846	NaN	0.320104	19.447952	3.774566	NaN	NaN	NaN	17.805773	inf	NaN	2.602986	0.402402	0.314370	0.732374	0.431759	0.000000	0.128112	NaN	NaN	0.047043	0.245280	NaN	NaN	NaN	1.636364	1.938710	1.000000	1.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	325.881333	88.693333	151.113333	55.86	29.09	60.370000	120.020000	2.286667	NaN	180.690000	24.633333	3.726667	NaN	NaN	NaN	27.373333	4.653333	NaN	210.720000	41.673333	103.496667	47.233333	190.416667	NaN	40.003333	NaN	NaN	121.340000	310.970000	NaN	NaN	NaN	15.333333	387.666667	71.000000	61.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	508.054	310.98	71.68	44.38	39.04	10.61	22.46	4.91	0.00	37.99	289.58	6.53	0.00	296.11	18.09	352.21	8.04	20.28	19.84	48.18	0.00	4.94	0.00	4.94	53.13	18	601	50	50	NaN	NaN	NaN	NaN	NaN	0.00	0.00	NaN	NaN	NaN	0	0	NaN	0.0	0
4	7000142493	238.174	50.31	76.96	50.31	67.64	0.00	117.96	0.00	0.00	0.00	0.00	0.00	127.28	88.49	104.13	0.00	198.04	0.00	0.00	0.00	0.25	198.29	3	196	56	50	1.0	56.0	1.0	0.0	56.0	0.00	0.00	0.00	0.00	0.0	0	0.0	309.876	149.44	124.26	0.00	0.00	149.44	124.26	0.00	0.00	241.33	0.00	9.31	0.00	9.31	0.00	0.00	241.33	233.81	109.54	1.50	337.94	0.86	1.93	0.00	1.93	338.81	0.00	0.00	0.48	6	350	110	110	1.0	56.0	1.0	0.0	56.0	0.00	0.00	0.00	0.00	0.0	0	1	0	0.0	0.00	809.686	283.64	293.10	0.00	0.00	283.64	283.78	0.00	0.00	567.45	0.00	9.31	0.00	9.31	0.00	0.00	576.77	427.98	320.51	1.50	750.01	0.86	2.18	0.00	3.04	753.54	0.00	0.00	0.48	14	833	276	270	1.0	56.0	1.0	0.0	56.0	0.00	0.00	0.00	0.00	0	1	0	0.0	0.00	269.895333	94.546667	97.700000	0.00	0.000000	94.546667	94.593333	0.000000	0.000000	189.150000	0.000000	3.103333	0.000000	3.103333	0.000000	0.00	192.256667	142.660000	106.836667	0.500000	250.003333	0.286667	0.726667	0.000000	1.013333	251.180000	0.000000	0.000000	0.160000	4.666667	277.666667	92.000000	90.000000	1.0	56.0	1.0	0.0	56.0	0.000000	0.000000	0.00	0.000	0.0	0.000000	0.333333	0.0	0.0	0.000000	0.768611	0.561362	1.352416	NaN	NaN	0.561362	1.352416	NaN	NaN	0.862553	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.862553	2.642219	0.950612	NaN	1.706423	inf	0.000000	NaN	NaN	3.440000	1.708659	NaN	NaN	NaN	0.500000	0.820000	1.000000	1.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	398.088000	167.210000	118.533333	NaN	NaN	167.210000	115.426667	NaN	NaN	281.650000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	284.756667	124.716667	146.153333	NaN	270.383333	1.000000	NaN	NaN	NaN	1.893333	271.436667	NaN	NaN	NaN	8.666667	416.333333	129.666667	127.666667	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	238.174	83.89	124.26	0.00	0.00	83.89	124.26	0.00	0.00	208.16	0.00	0.00	0.00	0.00	0.00	208.16	233.81	104.13	0.00	337.94	0.86	0.00	0.00	0.86	338.81	3	287	110	110	NaN	NaN	NaN	NaN	NaN	0.00	0.00	NaN	NaN	NaN	0	0	NaN	0.0	0

Dep. Variable:	churn_flag	No. Observations:	69999
Model:	Logit	Df Residuals:	69900
Method:	MLE	Df Model:	98
Date:	Wed, 13 Oct 2021	Pseudo R-squ.:	0.4658
Time:	11:39:26	Log-Likelihood:	-12318.
converged:	True	LL-Null:	-23060.
Covariance Type:	nonrobust	LLR p-value:	0.000

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	-2.4037	0.074	-32.374	0.000	-2.549	-2.258
min_arpu	-0.0018	0.000	-5.407	0.000	-0.002	-0.001
min_total_og_mou	0.0006	0.000	2.480	0.013	0.000	0.001
min_total_ic_mou	-0.0038	0.001	-7.011	0.000	-0.005	-0.003
min_total_rech_num	-0.0051	0.015	-0.348	0.728	-0.034	0.024
min_max_rech_amt	-0.0004	0.001	-0.597	0.550	-0.002	0.001
min_last_day_rch_amt	-0.0066	0.001	-6.244	0.000	-0.009	-0.005
min_vol_2g_mb	0.0013	0.001	1.930	0.054	-1.99e-05	0.003
min_vol_3g_mb	-0.0002	0.000	-0.924	0.355	-0.001	0.000
min_monthly_2g	-0.8820	0.401	-2.200	0.028	-1.668	-0.096
min_sachet_2g	-0.1448	0.089	-1.630	0.103	-0.319	0.029
min_monthly_3g	-0.1243	0.305	-0.407	0.684	-0.723	0.474
min_sachet_3g	0.1954	0.196	0.997	0.319	-0.189	0.579
min_vbc_3g	-6.556e-05	0.000	-0.181	0.856	-0.001	0.001
max_total_og_mou	-0.0003	0.000	-1.821	0.069	-0.001	2.49e-05
max_total_ic_mou	-0.0003	0.000	-0.727	0.467	-0.001	0.000
max_total_rech_num	-0.0035	0.011	-0.326	0.744	-0.024	0.017
max_total_rech_amt	3.426e-05	0.000	0.164	0.870	-0.000	0.000
max_max_rech_amt	0.0002	0.000	0.471	0.638	-0.001	0.001
max_last_day_rch_amt	-0.0002	0.000	-0.417	0.677	-0.001	0.001
max_vol_2g_mb	0.0007	0.000	2.061	0.039	3.61e-05	0.001
max_vol_3g_mb	-3.74e-05	5.42e-05	-0.691	0.490	-0.000	6.87e-05
max_monthly_2g	0.0883	0.206	0.429	0.668	-0.315	0.491
max_sachet_2g	-0.0003	0.053	-0.006	0.996	-0.105	0.104
max_monthly_3g	0.0946	0.178	0.531	0.596	-0.255	0.444
max_sachet_3g	-0.0536	0.117	-0.459	0.646	-0.283	0.175
max_vbc_3g	0.0003	0.000	2.804	0.005	9.29e-05	0.001
sum_onnet_mou	0.0009	0.001	0.980	0.327	-0.001	0.003
sum_ofnet_mou	0.0005	0.001	0.579	0.563	-0.001	0.002
sum_roam_ic_mou	0.0002	0.000	1.590	0.112	-4.18e-05	0.000
sum_roam_og_mou	-0.0003	0.001	-0.321	0.748	-0.002	0.002
sum_loc_og_t2t_mou	9.5307	1.962	4.858	0.000	5.686	13.376
sum_loc_og_t2m_mou	9.5309	1.962	4.858	0.000	5.686	13.376
sum_loc_og_t2f_mou	9.5312	1.962	4.858	0.000	5.686	13.377
sum_loc_og_t2c_mou	0.0019	0.001	1.706	0.088	-0.000	0.004
sum_loc_og_mou	-16.9798	2.476	-6.857	0.000	-21.833	-12.126
sum_std_og_t2t_mou	-6.5508	2.387	-2.745	0.006	-11.229	-1.873
sum_std_og_t2m_mou	-6.5503	2.387	-2.744	0.006	-11.228	-1.872
sum_std_og_t2f_mou	-6.5508	2.387	-2.745	0.006	-11.229	-1.873
sum_isd_og_mou	-7.4487	1.627	-4.577	0.000	-10.638	-4.259
sum_std_og_mou	-0.8984	3.006	-0.299	0.765	-6.789	4.992
sum_spl_og_mou	-7.4485	1.627	-4.577	0.000	-10.638	-4.259
sum_og_others	-7.4481	1.627	-4.576	0.000	-10.638	-4.258
sum_loc_ic_t2t_mou	18.2181	1.812	10.053	0.000	14.666	21.770
sum_loc_ic_t2m_mou	18.2180	1.812	10.052	0.000	14.666	21.770
sum_loc_ic_t2f_mou	18.2181	1.812	10.052	0.000	14.666	21.770
sum_loc_ic_mou	-14.0148	2.490	-5.629	0.000	-18.895	-9.135
sum_std_ic_t2t_mou	-7.3734	2.449	-3.010	0.003	-12.174	-2.573
sum_std_ic_t2m_mou	-7.3743	2.449	-3.011	0.003	-12.175	-2.573
sum_std_ic_t2f_mou	-7.3749	2.450	-3.011	0.003	-12.176	-2.574
sum_std_ic_mou	11.5772	3.106	3.727	0.000	5.489	17.665
sum_spl_ic_mou	3.6218	1.784	2.031	0.042	0.126	7.117
sum_isd_ic_mou	4.2039	1.758	2.392	0.017	0.759	7.649
sum_ic_others	4.2028	1.758	2.391	0.017	0.758	7.648
sum_total_rech_data	3.1226	1.73e+05	1.8e-05	1.000	-3.39e+05	3.39e+05
sum_max_rech_data	0.0003	0.000	0.825	0.409	-0.000	0.001
sum_count_rech_2g	-3.0951	1.73e+05	-1.79e-05	1.000	-3.39e+05	3.39e+05
sum_count_rech_3g	-3.1763	1.73e+05	-1.83e-05	1.000	-3.39e+05	3.39e+05
sum_av_rech_amt_data	-0.0005	0.000	-1.717	0.086	-0.001	7.63e-05
sum_arpu_3g	-0.0002	0.000	-0.584	0.559	-0.001	0.000
sum_arpu_2g	0.0004	0.000	0.937	0.349	-0.000	0.001
sum_night_pck_user	-0.0666	0.129	-0.515	0.606	-0.320	0.187
sum_fb_user	-0.0994	0.042	-2.365	0.018	-0.182	-0.017
mean_arpu	0.0024	0.000	5.160	0.000	0.001	0.003
mean_total_og_mou	22.3452	4.882	4.577	0.000	12.777	31.914
mean_total_ic_mou	-12.6064	5.273	-2.391	0.017	-22.941	-2.272
mean_total_rech_num	0.0292	0.024	1.235	0.217	-0.017	0.075
mean_total_rech_amt	0.0003	0.000	0.829	0.407	-0.000	0.001
mean_max_rech_amt	-0.0028	0.001	-2.684	0.007	-0.005	-0.001
mean_last_day_rch_amt	5.925e-05	0.001	0.052	0.959	-0.002	0.002
mean_vol_2g_mb	-0.0010	0.001	-1.228	0.219	-0.003	0.001
mean_monthly_2g	-0.3000	0.519	-0.577	0.564	-1.318	0.718
mean_sachet_3g	0.4129	0.495	0.833	0.405	-0.558	1.384
moq_arpu	5.59e-11	1.03e-11	5.452	0.000	3.58e-11	7.6e-11
moq_onnet_mou	1.988e-11	1.04e-11	1.906	0.057	-5.58e-13	4.03e-11
moq_ofnet_mou	4.561e-11	1.09e-11	4.174	0.000	2.42e-11	6.7e-11
moq_loc_og_t2t_mou	-1.947e-11	1.19e-11	-1.635	0.102	-4.28e-11	3.87e-12
moq_loc_og_t2m_mou	1.115e-11	2.04e-11	0.547	0.584	-2.88e-11	5.11e-11
moq_loc_og_mou	-6.32e-11	2.42e-11	-2.615	0.009	-1.11e-10	-1.58e-11
moq_total_og_mou	8.651e-11	1.36e-11	6.375	0.000	5.99e-11	1.13e-10
moq_loc_ic_t2t_mou	4.931e-11	1e-11	4.926	0.000	2.97e-11	6.89e-11
moq_loc_ic_t2m_mou	9.302e-11	1.9e-11	4.892	0.000	5.58e-11	1.3e-10
moq_loc_ic_mou	-1.441e-11	2.38e-11	-0.605	0.545	-6.11e-11	3.23e-11
moq_total_ic_mou	2.257e-10	1.54e-11	14.682	0.000	1.96e-10	2.56e-10
moq_total_rech_num	9.089e-11	1.04e-11	8.776	0.000	7.06e-11	1.11e-10
moq_max_rech_amt	1.651e-11	8.93e-12	1.848	0.065	-1e-12	3.4e-11
arpu_inlast	0.0008	0.000	3.263	0.001	0.000	0.001
total_og_mou_inlast	-0.0008	0.000	-5.887	0.000	-0.001	-0.001
total_ic_mou_inlast	-0.0022	0.000	-8.780	0.000	-0.003	-0.002
total_rech_num_inlast	-0.0326	0.008	-4.251	0.000	-0.048	-0.018
total_rech_amt_inlast	-0.0013	0.000	-5.463	0.000	-0.002	-0.001
max_rech_amt_inlast	0.0030	0.000	8.177	0.000	0.002	0.004
last_day_rch_amt_inlast	-0.0027	0.000	-7.743	0.000	-0.003	-0.002
vol_2g_mb_inlast	-0.0013	0.000	-4.558	0.000	-0.002	-0.001
vol_3g_mb_inlast	1.736e-05	0.000	0.157	0.875	-0.000	0.000
monthly_2g_inlast	-0.5191	0.164	-3.163	0.002	-0.841	-0.197
sachet_2g_inlast	-0.0758	0.032	-2.352	0.019	-0.139	-0.013
monthly_3g_inlast	-0.4936	0.139	-3.547	0.000	-0.766	-0.221
sachet_3g_inlast	-0.2953	0.092	-3.208	0.001	-0.476	-0.115
aug_vbc_3g_inlast	-0.0004	0.000	-2.318	0.020	-0.001	-6.79e-05

	actual	predicted_prob
71946	0	0.061049
21641	0	0.014883
82641	0	0.097555
14736	1	0.058771
50978	0	0.021138

Logistic Regression¶

Decision tree¶

Random Forest¶

Gradient Boosting¶

Scoring Data¶

	actual	predicted_prob
79667	0	0.001910
83841	0	0.011133
57025	0	0.040288
53130	0	0.013395
32612	0	0.396469

	importance
total_ic_mou_inlast	0.834796
last_day_rch_amt_inlast	0.066811
sum_fb_user	0.032386
min_total_ic_mou	0.029061
sum_roam_og_mou	0.018384
arpu_inlast	0.018053
total_og_mou_inlast	0.000509
sum_count_rech_2g	0.000000
mean_total_og_mou	0.000000
mean_sachet_3g	0.000000
mean_monthly_2g	0.000000
mean_vol_2g_mb	0.000000
mean_last_day_rch_amt	0.000000
mean_max_rech_amt	0.000000
mean_total_rech_amt	0.000000
mean_total_rech_num	0.000000
mean_total_ic_mou	0.000000
mean_arpu	0.000000
sum_count_rech_3g	0.000000
moq_arpu	0.000000
sum_total_rech_data	0.000000
sum_night_pck_user	0.000000
sum_arpu_2g	0.000000
sum_max_rech_data	0.000000
sum_arpu_3g	0.000000
sum_av_rech_amt_data	0.000000
min_arpu	0.000000
moq_loc_og_t2t_mou	0.000000
moq_onnet_mou	0.000000
moq_ofnet_mou	0.000000
sachet_3g_inlast	0.000000
monthly_3g_inlast	0.000000
sachet_2g_inlast	0.000000
monthly_2g_inlast	0.000000
vol_3g_mb_inlast	0.000000
vol_2g_mb_inlast	0.000000
max_rech_amt_inlast	0.000000
total_rech_amt_inlast	0.000000
total_rech_num_inlast	0.000000
moq_max_rech_amt	0.000000
moq_total_rech_num	0.000000
moq_total_ic_mou	0.000000
moq_loc_ic_mou	0.000000
moq_loc_ic_t2m_mou	0.000000
moq_loc_ic_t2t_mou	0.000000
moq_total_og_mou	0.000000
moq_loc_og_mou	0.000000
moq_loc_og_t2m_mou	0.000000
sum_isd_ic_mou	0.000000
sum_ic_others	0.000000
sum_std_ic_mou	0.000000
sum_spl_ic_mou	0.000000
min_total_og_mou	0.000000
max_monthly_3g	0.000000
max_sachet_2g	0.000000
max_monthly_2g	0.000000
max_vol_3g_mb	0.000000
max_vol_2g_mb	0.000000
max_last_day_rch_amt	0.000000
max_max_rech_amt	0.000000
max_total_rech_amt	0.000000
max_total_rech_num	0.000000
max_total_ic_mou	0.000000
max_total_og_mou	0.000000
min_vbc_3g	0.000000
min_sachet_3g	0.000000
min_monthly_3g	0.000000
min_sachet_2g	0.000000
min_monthly_2g	0.000000
min_vol_3g_mb	0.000000
min_vol_2g_mb	0.000000
min_last_day_rch_amt	0.000000
min_max_rech_amt	0.000000
min_total_rech_num	0.000000
max_sachet_3g	0.000000
max_vbc_3g	0.000000
sum_onnet_mou	0.000000
sum_std_og_mou	0.000000
sum_std_ic_t2f_mou	0.000000
sum_std_ic_t2m_mou	0.000000
sum_std_ic_t2t_mou	0.000000
sum_loc_ic_mou	0.000000
sum_loc_ic_t2f_mou	0.000000
sum_loc_ic_t2m_mou	0.000000
sum_loc_ic_t2t_mou	0.000000
sum_og_others	0.000000
sum_spl_og_mou	0.000000
sum_isd_og_mou	0.000000
sum_ofnet_mou	0.000000
sum_std_og_t2f_mou	0.000000
sum_std_og_t2m_mou	0.000000
sum_std_og_t2t_mou	0.000000
sum_loc_og_mou	0.000000
sum_loc_og_t2c_mou	0.000000
sum_loc_og_t2f_mou	0.000000
sum_loc_og_t2m_mou	0.000000
sum_loc_og_t2t_mou	0.000000
sum_roam_ic_mou	0.000000
aug_vbc_3g_inlast	0.000000

	predictions	Predicted_prob
79667	0	0.011566
83841	0	0.011566
57025	0	0.011566
53130	0	0.011566
32612	1	0.630846

	Actual	Predicted_prob
71946	0	0.042058
21641	0	0.042058
82641	0	0.051801
14736	1	0.168478
50978	0	0.051801

	importance
total_ic_mou_inlast	0.051833
moq_total_ic_mou	0.048813
moq_total_og_mou	0.039243
total_og_mou_inlast	0.037246
moq_loc_ic_mou	0.033884
moq_loc_ic_t2m_mou	0.033059
arpu_inlast	0.031210
moq_arpu	0.026114
min_total_ic_mou	0.024976
total_rech_amt_inlast	0.021459
moq_loc_ic_t2t_mou	0.019855
moq_max_rech_amt	0.019455
moq_loc_og_t2m_mou	0.019273
moq_loc_og_mou	0.018848
max_rech_amt_inlast	0.017562
min_arpu	0.016766
min_total_og_mou	0.015749
moq_ofnet_mou	0.015371
moq_total_rech_num	0.014726
sum_loc_ic_mou	0.014403
max_total_ic_mou	0.013894
last_day_rch_amt_inlast	0.013581
mean_total_ic_mou	0.013075
sum_loc_ic_t2m_mou	0.012898
moq_onnet_mou	0.012516
total_rech_num_inlast	0.012416
moq_loc_og_t2t_mou	0.011942
max_total_og_mou	0.011893
sum_loc_ic_t2t_mou	0.011769
mean_arpu	0.011686
mean_total_og_mou	0.011466
sum_loc_og_mou	0.010953
sum_ofnet_mou	0.010878
sum_roam_og_mou	0.010820
mean_total_rech_amt	0.010692
max_total_rech_amt	0.010567
sum_loc_og_t2m_mou	0.010558
sum_onnet_mou	0.010387
mean_total_rech_num	0.010154
mean_max_rech_amt	0.009946
sum_roam_ic_mou	0.009803
sum_loc_og_t2t_mou	0.009579
sum_std_og_mou	0.009297
mean_last_day_rch_amt	0.009293
sum_std_ic_mou	0.009266
max_total_rech_num	0.009021
sum_std_og_t2m_mou	0.008755
min_total_rech_num	0.008666
sum_std_ic_t2m_mou	0.008567
max_max_rech_amt	0.008224
sum_loc_ic_t2f_mou	0.008044
sum_std_og_t2t_mou	0.008031
sum_spl_og_mou	0.007666
min_max_rech_amt	0.007617
max_last_day_rch_amt	0.007599
sum_std_ic_t2t_mou	0.007375
sum_loc_og_t2c_mou	0.005653
sum_max_rech_data	0.005536
sum_loc_og_t2f_mou	0.005276
sum_av_rech_amt_data	0.005166
sum_total_rech_data	0.004700
sum_ic_others	0.004528
mean_vol_2g_mb	0.004515
max_vol_2g_mb	0.004361
vol_2g_mb_inlast	0.004178
sum_fb_user	0.004012
sum_isd_ic_mou	0.003973
sum_spl_ic_mou	0.003900
sum_arpu_2g	0.003705
sum_og_others	0.003673
min_last_day_rch_amt	0.003478
sum_std_ic_t2f_mou	0.003377
max_vol_3g_mb	0.003307
vol_3g_mb_inlast	0.003095
max_vbc_3g	0.002923
sum_arpu_3g	0.002868
sum_count_rech_2g	0.002835
sum_isd_og_mou	0.002652
sum_std_og_t2f_mou	0.002508
aug_vbc_3g_inlast	0.002491
max_sachet_2g	0.002129
sum_count_rech_3g	0.001822
min_vol_2g_mb	0.001797
sachet_2g_inlast	0.001668
min_vol_3g_mb	0.001629
min_vbc_3g	0.001310
mean_monthly_2g	0.001093
mean_sachet_3g	0.001086
max_sachet_3g	0.000991
max_monthly_3g	0.000936
max_monthly_2g	0.000919
monthly_3g_inlast	0.000633
monthly_2g_inlast	0.000589
min_sachet_2g	0.000579
sachet_3g_inlast	0.000505
min_monthly_3g	0.000326
sum_night_pck_user	0.000285
min_sachet_3g	0.000157
min_monthly_2g	0.000100

	Actual	predictions	Predicted_prob
71946	0	0	0.047
21641	0	0	0.011
82641	0	0	0.023
14736	1	1	0.678
50978	0	0	0.008

	importance
total_ic_mou_inlast	0.725564
moq_total_ic_mou	0.155562
sum_fb_user	0.022490
total_rech_amt_inlast	0.018681
last_day_rch_amt_inlast	0.015618
vol_2g_mb_inlast	0.010679
total_og_mou_inlast	0.009211
min_total_ic_mou	0.008298
min_max_rech_amt	0.006398
sum_roam_og_mou	0.004400
arpu_inlast	0.004227
moq_total_rech_num	0.003941
min_arpu	0.003937
max_total_rech_num	0.003696
sum_av_rech_amt_data	0.002978
total_rech_num_inlast	0.001458
moq_loc_ic_t2m_mou	0.001349
moq_total_og_mou	0.000698
moq_ofnet_mou	0.000576
sum_total_rech_data	0.000088
mean_sachet_3g	0.000049
max_vol_2g_mb	0.000038
min_sachet_3g	0.000029
min_total_rech_num	0.000026
moq_arpu	0.000010
mean_total_rech_amt	0.000000
mean_total_rech_num	0.000000
mean_total_ic_mou	0.000000
sum_night_pck_user	0.000000
mean_total_og_mou	0.000000
mean_arpu	0.000000
sum_count_rech_2g	0.000000
sum_count_rech_3g	0.000000
sum_arpu_3g	0.000000
mean_last_day_rch_amt	0.000000
sum_arpu_2g	0.000000
mean_max_rech_amt	0.000000
moq_loc_ic_t2t_mou	0.000000
mean_vol_2g_mb	0.000000
mean_monthly_2g	0.000000
moq_onnet_mou	0.000000
moq_loc_og_t2t_mou	0.000000
moq_loc_og_t2m_mou	0.000000
moq_loc_og_mou	0.000000
moq_loc_ic_mou	0.000000
moq_max_rech_amt	0.000000
max_rech_amt_inlast	0.000000
vol_3g_mb_inlast	0.000000
monthly_2g_inlast	0.000000
sachet_2g_inlast	0.000000
monthly_3g_inlast	0.000000
sachet_3g_inlast	0.000000
sum_max_rech_data	0.000000
sum_std_ic_mou	0.000000
sum_ic_others	0.000000
sum_roam_ic_mou	0.000000
sum_onnet_mou	0.000000
max_vbc_3g	0.000000
max_sachet_3g	0.000000
max_monthly_3g	0.000000
max_sachet_2g	0.000000
max_monthly_2g	0.000000
max_vol_3g_mb	0.000000
max_last_day_rch_amt	0.000000
max_max_rech_amt	0.000000
max_total_rech_amt	0.000000
max_total_ic_mou	0.000000
max_total_og_mou	0.000000
min_vbc_3g	0.000000
min_monthly_3g	0.000000
min_sachet_2g	0.000000
min_monthly_2g	0.000000
min_vol_3g_mb	0.000000
min_vol_2g_mb	0.000000
min_last_day_rch_amt	0.000000
sum_ofnet_mou	0.000000
sum_loc_og_t2t_mou	0.000000
sum_isd_ic_mou	0.000000
sum_loc_og_t2m_mou	0.000000
sum_spl_ic_mou	0.000000
min_total_og_mou	0.000000
sum_std_ic_t2f_mou	0.000000
sum_std_ic_t2m_mou	0.000000
sum_std_ic_t2t_mou	0.000000
sum_loc_ic_mou	0.000000
sum_loc_ic_t2f_mou	0.000000
sum_loc_ic_t2m_mou	0.000000
sum_loc_ic_t2t_mou	0.000000
sum_og_others	0.000000
sum_spl_og_mou	0.000000
sum_std_og_mou	0.000000
sum_isd_og_mou	0.000000
sum_std_og_t2f_mou	0.000000
sum_std_og_t2m_mou	0.000000
sum_std_og_t2t_mou	0.000000
sum_loc_og_mou	0.000000
sum_loc_og_t2c_mou	0.000000
sum_loc_og_t2f_mou	0.000000
aug_vbc_3g_inlast	0.000000

	actual	predicted_prob_LG	Predicted_prob_DT	Predicted_prob_RF	Predicted_prob_GB
0	1	0.013544	0.011566	0.011566	0.063751
1	0	0.014853	0.011566	0.011566	0.046762
2	0	0.019256	0.011566	0.011566	0.046762
3	0	0.019448	0.042058	0.042058	0.065506
4	0	0.013349	0.011566	0.011566	0.046762
...	...	...	...	...	...
99994	0	0.038397	0.051801	0.051801	0.063736
99995	0	0.022726	0.011566	0.011566	0.047014
99996	1	0.913425	0.844611	0.844611	0.597711
99997	0	0.001395	0.011566	0.011566	0.063751
99998	1	0.943300	0.844611	0.844611	0.597711

	actual	Avg_prob
0	1	0.025107
1	0	0.021187
2	0	0.022288
3	0	0.042267
4	0	0.020811
...	...	...
99994	0	0.051434
99995	0	0.023218
99996	1	0.800089
99997	0	0.022069
99998	1	0.807558

	Defaulter	Non-Defaulter	Probability
	sum	sum	amax	amin
Decile
1	46	9954	0.019106	0.017474
2	46	9954	0.021157	0.019106
3	82	9918	0.023776	0.021158
4	110	9890	0.029618	0.023776
5	198	9802	0.039916	0.029619
6	256	9743	0.045823	0.039917
7	324	9676	0.051612	0.045823
8	691	9309	0.068179	0.051613
9	1553	8447	0.174170	0.068183
10	6885	3115	0.821733	0.174174

	min_arpu	min_onnet_mou	min_ofnet_mou	min_roam_ic_mou	min_roam_og_mou	min_loc_og_t2t_mou	min_loc_og_t2m_mou	min_loc_og_t2f_mou	min_loc_og_t2c_mou	min_loc_og_mou	min_std_og_t2t_mou	min_std_og_t2m_mou	min_std_og_t2f_mou	min_std_og_t2c_mou	min_isd_og_mou	min_std_og_mou	min_spl_og_mou	min_og_others	min_total_og_mou	min_loc_ic_t2t_mou	min_loc_ic_t2m_mou	min_loc_ic_t2f_mou	min_loc_ic_mou	min_std_ic_t2t_mou	min_std_ic_t2m_mou	min_std_ic_t2f_mou	min_std_ic_t2o_mou	min_std_ic_mou	min_total_ic_mou	min_spl_ic_mou	min_isd_ic_mou	min_ic_others	min_total_rech_num	min_total_rech_amt	min_max_rech_amt	min_last_day_rch_amt	min_vol_2g_mb	min_vol_3g_mb	min_monthly_2g	min_sachet_2g	min_monthly_3g	min_sachet_3g	min_vbc_3g	max_arpu	max_onnet_mou	max_ofnet_mou	max_roam_ic_mou	max_roam_og_mou	max_loc_og_t2t_mou	max_loc_og_t2m_mou	max_loc_og_t2f_mou	max_loc_og_t2c_mou	max_loc_og_mou	max_std_og_t2t_mou	max_std_og_t2m_mou	max_std_og_t2f_mou	max_std_og_t2c_mou	max_isd_og_mou	max_std_og_mou	max_spl_og_mou	max_og_others	max_total_og_mou	max_loc_ic_t2t_mou	max_loc_ic_t2m_mou	max_loc_ic_t2f_mou	max_loc_ic_mou	max_std_ic_t2t_mou	max_std_ic_t2m_mou	max_std_ic_t2f_mou	max_std_ic_t2o_mou	max_std_ic_mou	max_total_ic_mou	max_spl_ic_mou	max_isd_ic_mou	max_ic_others	max_total_rech_num	max_total_rech_amt	max_max_rech_amt	max_last_day_rch_amt	max_vol_2g_mb	max_vol_3g_mb	max_monthly_2g	max_sachet_2g	max_monthly_3g	max_sachet_3g	max_vbc_3g	sum_arpu	sum_onnet_mou	sum_ofnet_mou	sum_roam_ic_mou	sum_roam_og_mou	sum_loc_og_t2t_mou	sum_loc_og_t2m_mou	sum_loc_og_t2f_mou	sum_loc_og_t2c_mou	sum_loc_og_mou	sum_std_og_t2t_mou	sum_std_og_t2m_mou	sum_std_og_t2f_mou	sum_std_og_t2c_mou	sum_isd_og_mou	sum_std_og_mou	sum_spl_og_mou	sum_og_others	sum_total_og_mou	sum_loc_ic_t2t_mou	sum_loc_ic_t2m_mou	sum_loc_ic_t2f_mou	sum_loc_ic_mou	sum_std_ic_t2t_mou	sum_std_ic_t2m_mou	sum_std_ic_t2f_mou	sum_std_ic_t2o_mou	sum_std_ic_mou	sum_total_ic_mou	sum_spl_ic_mou	sum_isd_ic_mou	sum_ic_others	sum_total_rech_num	sum_total_rech_amt	sum_max_rech_amt	sum_last_day_rch_amt	sum_total_rech_data	sum_max_rech_data	sum_count_rech_2g	sum_count_rech_3g	sum_av_rech_amt_data	sum_vol_2g_mb	sum_vol_3g_mb	sum_arpu_3g	sum_arpu_2g	sum_night_pck_user	sum_monthly_2g	sum_sachet_2g	sum_monthly_3g	sum_sachet_3g	sum_fb_user	sum_vbc_3g	mean_arpu	mean_onnet_mou	mean_ofnet_mou	mean_roam_ic_mou	mean_roam_og_mou	mean_loc_og_t2t_mou	mean_loc_og_t2m_mou	mean_loc_og_t2f_mou	mean_loc_og_t2c_mou	mean_loc_og_mou	mean_std_og_t2t_mou	mean_std_og_t2m_mou	mean_std_og_t2f_mou	mean_std_og_t2c_mou	mean_isd_og_mou	mean_std_og_mou	mean_spl_og_mou	mean_og_others	mean_total_og_mou	mean_loc_ic_t2t_mou	mean_loc_ic_t2m_mou	mean_loc_ic_t2f_mou	mean_loc_ic_mou	mean_std_ic_t2t_mou	mean_std_ic_t2m_mou	mean_std_ic_t2f_mou	mean_std_ic_t2o_mou	mean_std_ic_mou	mean_total_ic_mou	mean_spl_ic_mou	mean_isd_ic_mou	mean_ic_others	mean_total_rech_num	mean_total_rech_amt	mean_max_rech_amt	mean_last_day_rch_amt	mean_vol_2g_mb	mean_vol_3g_mb	mean_monthly_2g	mean_sachet_2g	mean_monthly_3g	mean_sachet_3g	mean_vbc_3g	mom_arpu	mom_onnet_mou	mom_ofnet_mou	mom_loc_og_t2t_mou	mom_loc_og_t2m_mou	mom_loc_og_mou	mom_total_og_mou	mom_loc_ic_t2t_mou	mom_loc_ic_t2m_mou	mom_loc_ic_mou	mom_std_ic_mou	mom_total_ic_mou	mom_total_rech_num	mom_total_rech_amt	mom_max_rech_amt	mom_last_day_rch_amt	moq_arpu	moq_onnet_mou	moq_ofnet_mou	moq_loc_og_t2t_mou	moq_loc_og_t2m_mou	moq_loc_og_mou	moq_total_og_mou	moq_loc_ic_t2t_mou	moq_loc_ic_t2m_mou	moq_loc_ic_mou	moq_total_ic_mou	moq_total_rech_num	moq_total_rech_amt	moq_max_rech_amt	arpu_inlast	onnet_mou_inlast	ofnet_mou_inlast	roam_ic_mou_inlast	roam_og_mou_inlast	loc_og_t2t_mou_inlast	loc_og_t2m_mou_inlast	loc_og_t2f_mou_inlast	loc_og_t2c_mou_inlast	loc_og_mou_inlast	std_og_t2t_mou_inlast	std_og_t2m_mou_inlast	std_og_t2f_mou_inlast	std_og_t2c_mou_inlast	std_og_mou_inlast	isd_og_mou_inlast	spl_og_mou_inlast	og_others_inlast	total_og_mou_inlast	loc_ic_t2t_mou_inlast	loc_ic_t2m_mou_inlast	loc_ic_t2f_mou_inlast	loc_ic_mou_inlast	std_ic_t2t_mou_inlast	std_ic_t2m_mou_inlast	std_ic_t2f_mou_inlast	std_ic_t2o_mou_inlast	std_ic_mou_inlast	total_ic_mou_inlast	spl_ic_mou_inlast	isd_ic_mou_inlast	ic_others_inlast	total_rech_num_inlast	total_rech_amt_inlast	max_rech_amt_inlast	last_day_rch_amt_inlast	vol_2g_mb_inlast	vol_3g_mb_inlast	monthly_2g_inlast	sachet_2g_inlast	monthly_3g_inlast	sachet_3g_inlast	aug_vbc_3g_inlast
min_arpu	1.000000	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	0.676357	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	0.335704	-0.055562	-0.055562	-0.055562	4.996630e-01	0.964394	0.589338	0.376537	0.076647	0.232448	0.087483	0.052473	0.224248	0.066380	0.211992	0.768409	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	0.490560	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	0.258845	-0.055562	-0.055562	-0.055562	0.306744	0.765558	0.337101	0.327023	0.074490	0.225005	0.093971	0.059940	0.230074	0.078414	0.208438	0.919062	0.389736	0.560122	0.079568	0.204610	0.252485	0.459546	0.218918	0.052717	0.455006	0.291995	0.313301	0.100733	NaN	0.414327	0.394516	0.108297	0.034910	0.602305	0.134095	0.282906	0.100491	0.276574	0.100399	0.113289	0.054512	NaN	0.142973	0.305063	-0.022876	0.074368	0.029725	0.411452	0.908136	0.506333	0.437871	0.142060	0.257449	0.078560	0.193912	0.286046	0.083412	0.244936	0.294309	0.280538	0.044235	0.107359	0.060734	0.251539	0.080560	0.199310	0.226214	0.919062	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	0.602305	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	0.305063	-0.055562	-0.055562	-0.055562	0.411452	0.908136	0.506333	0.437871	0.083412	0.244936	0.107359	0.060734	0.251539	0.080560	0.226214	-0.092730	-0.208598	-0.186376	-0.228884	-0.204923	-0.200443	-0.134992	-0.209751	-0.193989	-0.188221	-0.247959	-0.126364	-0.062342	-0.156941	-0.156941	-0.235389	-0.172954	-0.258036	-0.233339	-0.265356	-0.247850	-0.244245	-0.193490	-0.238056	-0.223823	-0.221250	-0.177627	-0.116583	-0.241174	-0.241174	0.827901	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	0.530158	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	0.291950	-0.153633	-0.153633	-0.153633	0.335493	0.806694	0.433091	0.310998	0.069492	0.225912	8.771400e-02	0.065082	0.221977	0.067174	0.203261
min_onnet_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338554e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067047	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665636e-02	-0.023631	0.055777	0.000891	-0.004021
min_ofnet_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338551e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048796	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048796	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067047	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_roam_ic_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_roam_og_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_loc_og_t2t_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338560e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_loc_og_t2m_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338558e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665634e-02	-0.023631	0.055777	0.000891	-0.004021
min_loc_og_t2f_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_loc_og_t2c_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_loc_og_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338556e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057099	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286523	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067047	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665634e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_og_t2t_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338556e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067047	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665636e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_og_t2m_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338556e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067047	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665636e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_og_t2f_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_og_t2c_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_isd_og_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_og_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338549e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080153	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049396	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067047	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665636e-02	-0.023631	0.055777	0.000891	-0.004021
min_spl_og_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_og_others	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_total_og_mou	0.676357	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	1.000000	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	0.298090	-0.059790	-0.059790	-0.059790	4.736177e-01	0.663686	0.357694	0.282474	-0.007357	0.021019	-0.001772	-0.010274	0.028477	0.002716	0.034017	0.503449	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	0.715438	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	0.207144	-0.059790	-0.059790	-0.059790	0.293736	0.499377	0.198956	0.207562	-0.020543	0.011914	-0.011347	-0.002127	0.029556	-0.001595	0.017032	0.614246	0.652848	0.689267	-0.006559	0.027606	0.419227	0.478667	0.151966	0.080777	0.564025	0.516434	0.524839	0.072250	NaN	0.071311	0.676568	0.141954	0.033697	0.877865	0.129756	0.230458	0.071802	0.234941	0.098772	0.106905	0.033457	NaN	0.134267	0.255798	-0.009221	0.024956	0.021932	0.391827	0.610221	0.301909	0.292500	-0.000101	0.015589	-0.007698	0.016781	0.017957	-0.017103	0.017160	0.038087	0.033020	0.024218	-0.007908	-0.006414	0.033077	0.000038	-0.013050	0.024914	0.614246	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	0.877865	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	0.255798	-0.059790	-0.059790	-0.059790	0.391827	0.610221	0.301909	0.292500	-0.017103	0.017160	-0.007908	-0.006414	0.033077	0.000038	0.024914	-0.073885	-0.204506	-0.172369	-0.229647	-0.203596	-0.200684	-0.146153	-0.199603	-0.180715	-0.177637	-0.240396	-0.129278	-0.048101	-0.126719	-0.126719	-0.195523	-0.137694	-0.249228	-0.213664	-0.265157	-0.246690	-0.245922	-0.202929	-0.228751	-0.216284	-0.214166	-0.180034	-0.087935	-0.193325	-0.193325	0.546049	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	0.770610	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	0.244973	-0.135221	-0.135221	-0.135221	0.324901	0.526085	0.259057	0.201586	-0.011029	0.020431	-7.192783e-03	0.009023	0.031398	0.000492	0.025217
min_loc_ic_t2t_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_loc_ic_t2m_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338559e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286523	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665634e-02	-0.023631	0.055777	0.000891	-0.004021
min_loc_ic_t2f_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_loc_ic_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338558e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080716	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033522	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286523	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665634e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_ic_t2t_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_ic_t2m_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_ic_t2f_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_ic_t2o_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_ic_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_total_ic_mou	0.335704	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	0.298090	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	1.000000	-0.067235	-0.067235	-0.067235	1.932729e-01	0.311132	0.277172	0.199727	0.019946	0.062818	0.061683	0.006526	0.062735	0.009841	0.099481	0.205413	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	0.138628	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	0.810716	-0.067235	-0.067235	-0.067235	0.060802	0.208597	0.159218	0.157370	-0.000561	0.049181	0.063978	-0.002730	0.068279	0.004831	0.078245	0.277159	0.087705	0.224308	-0.007777	-0.039661	0.230359	0.426904	0.208992	0.017728	0.420466	-0.031211	-0.002343	0.084505	NaN	0.021251	-0.019977	0.034384	0.020360	0.212294	0.592536	0.721878	0.355216	0.858299	0.226325	0.314546	0.170617	NaN	0.373819	0.926438	-0.009584	0.247076	0.127384	0.122056	0.270575	0.238848	0.211459	0.027777	0.105663	0.013319	0.042438	0.091101	0.008821	0.059343	0.081226	0.072747	-0.005018	0.073470	0.000503	0.073219	0.006490	0.091582	0.092841	0.277159	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	0.212294	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	0.926438	-0.067235	-0.067235	-0.067235	0.122056	0.270575	0.238848	0.211459	0.008821	0.059343	0.073470	0.000503	0.073219	0.006490	0.092841	-0.079506	-0.207784	-0.186910	-0.246115	-0.220509	-0.216441	-0.157271	-0.236623	-0.205726	-0.202242	-0.202406	-0.148286	-0.053556	-0.122679	-0.122679	-0.160902	-0.143732	-0.238755	-0.220562	-0.278202	-0.260935	-0.258428	-0.212657	-0.276033	-0.249222	-0.244297	-0.204781	-0.079146	-0.172337	-0.172337	0.246415	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	0.180827	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	0.848240	-0.152059	-0.152059	-0.152059	0.082746	0.236127	0.204889	0.166513	0.005950	0.056111	5.854484e-02	0.006389	0.066374	0.004127	0.084863
min_spl_ic_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_isd_ic_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_ic_others	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_total_rech_num	0.499663	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083385	-0.083386	-0.083386	0.473618	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	0.193273	-0.083386	-0.083386	-0.083386	1.000000e+00	0.512838	0.087268	0.056482	0.090565	0.076861	-0.005156	0.283671	0.024433	0.135066	0.046977	0.348330	-0.083385	-0.083385	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083385	-0.083386	-0.083386	0.358727	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	0.148199	-0.083386	-0.083386	-0.083386	0.718878	0.325333	-0.099331	-0.113954	0.085540	0.072212	-0.006611	0.280629	0.024557	0.160573	0.047284	0.442386	0.310982	0.359569	0.017414	0.069273	0.146902	0.230918	-0.000175	0.095930	0.234734	0.272876	0.280266	-0.016791	NaN	0.049168	0.358312	0.164119	0.053224	0.434291	0.086578	0.173790	0.015271	0.164868	0.067865	0.069469	-0.011685	NaN	0.084152	0.176126	0.053124	0.017354	0.021330	0.885770	0.435979	-0.031525	-0.063586	0.311724	0.020913	0.297023	0.149534	0.116155	0.097345	0.080270	0.062408	0.068898	0.097307	-0.005048	0.302450	0.028002	0.163884	0.152619	0.051591	0.442386	-0.083386	-0.083385	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083385	-0.083386	-0.083386	0.434291	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	0.176126	-0.083386	-0.083386	-0.083386	0.885770	0.435979	-0.031525	-0.063586	0.097345	0.080270	-0.005048	0.302450	0.028002	0.163884	0.051591	-0.114484	-0.220674	-0.207499	-0.229592	-0.221458	-0.217781	-0.161142	-0.217649	-0.207361	-0.204912	-0.211237	-0.150983	-0.089420	-0.187653	-0.187653	-0.269312	-0.179218	-0.255304	-0.243695	-0.248906	-0.251668	-0.250337	-0.206045	-0.230207	-0.231017	-0.229084	-0.188925	-0.189110	-0.278238	-0.278238	0.404814	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	0.390963	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	0.175429	-0.175256	-0.175256	-0.175256	0.771709	0.394760	-0.007570	-0.042457	0.085833	0.080892	9.335822e-07	0.276418	0.030137	0.144431	0.052325
min_total_rech_amt	0.964394	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	0.663686	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	0.311132	-0.050630	-0.050630	-0.050630	5.128384e-01	1.000000	0.612488	0.391196	0.077596	0.225374	0.085900	0.055809	0.213963	0.066707	0.196871	0.758824	-0.050630	-0.050629	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	0.490914	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	0.241335	-0.050630	-0.050630	-0.050630	0.320943	0.749758	0.279095	0.294792	0.077604	0.217056	0.092131	0.066736	0.216601	0.079979	0.195840	0.900323	0.389971	0.552282	0.074217	0.202029	0.241237	0.439401	0.181828	0.055309	0.433458	0.298686	0.319302	0.084200	NaN	0.418362	0.402412	0.106030	0.032260	0.597435	0.126610	0.263487	0.081806	0.256671	0.095212	0.106509	0.044561	NaN	0.133839	0.283693	-0.018102	0.070091	0.026300	0.425561	0.910293	0.470941	0.422077	0.144438	0.244316	0.084080	0.187848	0.275737	0.085757	0.236672	0.279032	0.266707	0.045600	0.105541	0.066660	0.237719	0.081692	0.195075	0.211612	0.900323	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	0.597435	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	0.283693	-0.050630	-0.050630	-0.050630	0.425561	0.910293	0.470941	0.422077	0.085757	0.236672	0.105541	0.066660	0.237719	0.081692	0.211612	-0.084322	-0.189506	-0.169274	-0.209849	-0.186872	-0.182685	-0.122945	-0.193848	-0.178182	-0.172553	-0.236278	-0.115955	-0.058937	-0.156761	-0.156761	-0.248103	-0.157156	-0.236348	-0.212333	-0.244965	-0.227537	-0.224301	-0.178373	-0.221221	-0.207859	-0.204847	-0.165060	-0.124968	-0.256104	-0.256104	0.815102	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	0.531203	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	0.273486	-0.140188	-0.140188	-0.140188	0.351024	0.815077	0.412798	0.311263	0.072159	0.219267	8.685290e-02	0.069704	0.210436	0.068167	0.190717
min_max_rech_amt	0.589338	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	0.357694	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	0.277172	-0.028164	-0.028164	-0.028164	8.726779e-02	0.612488	1.000000	0.487148	0.101765	0.302275	0.193625	-0.023402	0.290054	0.016342	0.276263	0.474339	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	0.232218	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	0.208173	-0.028164	-0.028164	-0.028164	-0.044900	0.482793	0.494463	0.520681	0.091985	0.285956	0.171054	-0.029632	0.280020	0.020607	0.275612	0.558551	0.181445	0.303717	0.068567	0.143148	0.179499	0.347191	0.269504	0.014837	0.342628	0.093339	0.106661	0.140209	NaN	0.121038	0.132510	0.021411	0.007710	0.302969	0.104736	0.228018	0.129295	0.228709	0.068104	0.088986	0.071469	NaN	0.111192	0.248562	-0.050262	0.057126	0.017101	0.009291	0.574143	0.789696	0.652110	0.072559	0.426078	0.006338	0.173856	0.366991	0.105037	0.313653	0.357452	0.344143	0.010496	0.206549	-0.030168	0.309093	0.020990	0.269283	0.297140	0.558551	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	0.302969	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	0.248562	-0.028164	-0.028164	-0.028164	0.009291	0.574143	0.789696	0.652110	0.105037	0.313653	0.206549	-0.030168	0.309093	0.020990	0.297140	-0.099689	-0.169072	-0.156870	-0.190860	-0.173393	-0.170008	-0.098859	-0.188112	-0.176466	-0.174743	-0.192081	-0.108842	-0.069636	-0.186279	-0.186279	-0.217779	-0.185157	-0.220869	-0.204086	-0.229751	-0.217366	-0.215166	-0.162698	-0.219075	-0.210583	-0.210074	-0.165613	-0.147536	-0.302355	-0.302355	0.506199	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	0.270079	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	0.239982	-0.142786	-0.142786	-0.142786	-0.009632	0.516607	0.675003	0.509713	0.082684	0.282508	1.686046e-01	-0.017247	0.268681	0.013476	0.263016
min_last_day_rch_amt	0.376537	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	0.282474	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	0.199727	-0.020047	-0.020047	-0.020047	5.648191e-02	0.391196	0.487148	1.000000	0.032742	0.168853	0.070315	-0.023268	0.165662	0.007618	0.129967	0.302555	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	0.186680	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	0.143195	-0.020047	-0.020047	-0.020047	-0.027573	0.301572	0.223366	0.328548	0.024709	0.144829	0.071690	-0.049566	0.153123	0.002196	0.120230	0.355913	0.147303	0.241481	0.035254	0.123654	0.127435	0.286173	0.182864	-0.000331	0.267905	0.083580	0.088560	0.093024	NaN	0.038419	0.113479	0.009760	-0.006396	0.241591	0.075623	0.177210	0.079764	0.171317	0.031792	0.045259	0.043336	NaN	0.056172	0.174593	-0.033692	0.033996	0.009835	0.007219	0.363362	0.372349	0.634567	0.011263	0.191601	-0.027856	0.090842	0.185127	0.030796	0.165051	0.200369	0.193495	-0.010511	0.082346	-0.042874	0.172715	0.004095	0.104608	0.133998	0.355913	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	0.241591	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	0.174593	-0.020047	-0.020047	-0.020047	0.007219	0.363362	0.372349	0.634567	0.030796	0.165051	0.082346	-0.042874	0.172715	0.004095	0.133998	-0.061144	-0.114053	-0.099325	-0.131219	-0.110525	-0.107738	-0.061763	-0.121883	-0.108561	-0.107215	-0.124129	-0.065687	-0.042782	-0.113029	-0.113029	-0.255072	-0.114311	-0.149352	-0.132351	-0.160676	-0.145165	-0.141774	-0.107673	-0.148006	-0.136299	-0.134640	-0.105604	-0.090800	-0.186082	-0.186082	0.333286	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	0.224944	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	0.171818	-0.091610	-0.091610	-0.091610	-0.009612	0.329150	0.323961	0.473784	0.025612	0.156178	6.580133e-02	-0.035692	0.148172	0.003064	0.128243
min_vol_2g_mb	0.076647	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.007357	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	0.019946	-0.011736	-0.011736	-0.011736	9.056481e-02	0.077596	0.101765	0.032742	1.000000	0.030777	0.323651	0.261002	0.039305	0.032165	0.081884	0.043891	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.030010	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	0.010275	-0.011736	-0.011736	-0.011736	0.052094	0.040609	0.031369	0.016319	0.623823	0.039355	0.265939	0.235053	0.056148	0.054655	0.086722	0.061404	-0.023527	-0.012073	-0.000815	-0.003175	0.001651	0.002791	-0.003084	0.019184	0.002595	-0.028106	-0.017271	-0.002119	NaN	-0.001307	-0.029429	0.016924	0.001655	-0.022674	0.008832	0.014859	0.010504	0.016330	-0.000619	0.001912	0.002444	NaN	0.001491	0.014411	-0.007671	-0.001356	0.003242	0.071953	0.060244	0.068357	0.028520	0.295923	0.245127	0.315964	0.066678	0.262129	0.813194	0.038860	0.051884	0.087788	0.078030	0.331902	0.261969	0.053145	0.048148	0.348262	0.090612	0.061404	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.022674	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	0.014411	-0.011736	-0.011736	-0.011736	0.071953	0.060244	0.068357	0.028520	0.813194	0.038860	0.331902	0.261969	0.053145	0.048148	0.090612	-0.021083	0.021850	-0.004523	0.013409	0.005328	0.005893	0.008635	-0.024635	-0.030025	-0.031518	0.009014	-0.020626	-0.015355	-0.039345	-0.039345	-0.048842	-0.039799	0.014207	-0.004109	0.003352	0.000767	-0.000378	-0.001181	-0.027856	-0.037250	-0.039074	-0.033774	-0.032392	-0.063195	-0.063195	0.054542	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.018129	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	0.016606	-0.031426	-0.031426	-0.031426	0.058414	0.052828	0.053464	0.018060	0.671698	0.034701	2.673638e-01	0.220654	0.042999	0.041474	0.081104
min_vol_3g_mb	0.232448	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.021019	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.062818	0.030278	0.030278	0.030278	7.686079e-02	0.225374	0.302275	0.168853	0.030777	1.000000	0.186316	0.069230	0.596986	0.234633	0.566077	0.209462	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	-0.009302	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.046640	0.030278	0.030278	0.030278	0.032231	0.200523	0.191708	0.171777	0.047174	0.737854	0.169326	0.067212	0.496777	0.209536	0.519336	0.231960	-0.005590	0.010451	0.009346	0.006528	0.028703	0.056610	0.039093	0.006914	0.055262	-0.023977	-0.027256	0.010774	NaN	0.003441	-0.033012	0.004682	-0.002708	0.002544	0.021165	0.050939	0.031128	0.050226	0.016865	0.012750	0.019992	NaN	0.020886	0.055304	-0.028787	0.022172	0.004368	0.053781	0.225440	0.272401	0.211950	0.280561	0.602773	0.106558	0.490561	0.721084	0.043110	0.884085	0.711248	0.719830	0.119795	0.201882	0.072409	0.580825	0.237844	0.380896	0.573230	0.231960	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.002544	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.055304	0.030278	0.030278	0.030278	0.053781	0.225440	0.272401	0.211950	0.043110	0.884085	0.201882	0.072409	0.580825	0.237844	0.573230	-0.019448	0.032433	0.021705	0.018231	0.020539	0.021257	0.033351	-0.007812	-0.009696	-0.009697	0.002193	0.006982	-0.016749	-0.037563	-0.037563	-0.039321	-0.038388	0.014049	0.008473	-0.000587	0.001065	0.002904	0.006543	-0.023338	-0.021736	-0.022660	-0.015598	-0.035579	-0.063498	-0.063498	0.201689	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	0.004060	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	0.054201	-0.006938	-0.006938	-0.006938	0.036448	0.192746	0.219319	0.149561	0.033435	0.774063	1.619486e-01	0.063467	0.482757	0.188229	0.481688
min_monthly_2g	0.087483	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.001772	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	0.061683	-0.012670	-0.012670	-0.012670	-5.156182e-03	0.085900	0.193625	0.070315	0.323651	0.186316	1.000000	-0.022098	-0.022708	-0.007935	0.212343	0.042832	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.030597	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	0.049863	-0.012670	-0.012670	-0.012670	-0.040424	0.041807	0.065560	0.060236	0.265079	0.156887	0.516972	-0.026537	-0.014414	-0.015059	0.194950	0.064437	-0.025897	-0.008310	-0.003835	-0.012511	0.014331	0.032878	0.028973	-0.006430	0.031025	-0.037982	-0.030900	0.007364	NaN	-0.000426	-0.044490	-0.011706	-0.006994	-0.020894	0.031717	0.048840	0.048108	0.057290	0.005417	0.009656	0.010953	NaN	0.011558	0.056903	-0.024521	0.016782	0.003290	-0.028460	0.063582	0.132024	0.081493	0.081141	0.350145	0.105072	-0.022537	0.241434	0.313045	0.179186	-0.006640	-0.009329	-0.001599	0.759557	-0.027926	-0.021697	-0.013985	0.366056	0.216033	0.064437	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.020894	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	0.056903	-0.012670	-0.012670	-0.012670	-0.028460	0.063582	0.132024	0.081493	0.313045	0.179186	0.759557	-0.027926	-0.021697	-0.013985	0.216033	-0.019869	0.016038	-0.000588	0.000912	-0.003634	-0.003168	0.011204	-0.033834	-0.039880	-0.039335	-0.003704	-0.027846	-0.016171	-0.039264	-0.039264	-0.036851	-0.038799	0.001676	-0.003521	-0.013818	-0.012654	-0.012455	-0.000007	-0.044168	-0.048020	-0.048359	-0.040651	-0.034352	-0.064591	-0.064591	0.060108	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.015914	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	0.054148	-0.030379	-0.030379	-0.030379	-0.031623	0.059045	0.107749	0.060981	0.246176	0.163155	6.278501e-01	-0.025757	-0.018057	-0.013190	0.199682
min_sachet_2g	0.052473	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.010274	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	0.006526	-0.014647	-0.014647	-0.014647	2.836708e-01	0.055809	-0.023402	-0.023268	0.261002	0.069230	-0.022098	1.000000	-0.021589	0.140426	0.052414	0.012608	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.031108	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	0.000318	-0.014647	-0.014647	-0.014647	0.209661	0.008396	-0.059612	-0.067855	0.199639	0.059688	-0.015895	0.698883	-0.019143	0.173477	0.052223	0.031717	-0.015979	-0.024290	-0.008613	-0.017378	0.008315	-0.012168	-0.024369	0.024526	-0.004010	-0.022390	-0.019377	-0.015055	NaN	-0.002411	-0.027371	0.026492	0.005919	-0.024234	0.002473	0.011406	-0.005064	0.008107	-0.004258	-0.006663	-0.010202	NaN	-0.008623	0.003291	-0.002293	-0.002985	0.000378	0.256594	0.030555	-0.054818	-0.064195	0.770706	0.039284	0.841312	0.132892	0.235866	0.241053	0.067269	0.016247	0.029566	0.080134	-0.022360	0.858112	-0.022696	0.174819	0.344206	0.056544	0.031717	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.024234	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	0.003291	-0.014647	-0.014647	-0.014647	0.256594	0.030555	-0.054818	-0.064195	0.241053	0.067269	-0.022360	0.858112	-0.022696	0.174819	0.056544	-0.023418	0.015925	-0.017745	0.000432	-0.007395	-0.007419	-0.011987	-0.042014	-0.045157	-0.045539	0.015337	-0.032754	-0.016532	-0.043026	-0.043026	-0.066711	-0.043640	0.010071	-0.016432	-0.006464	-0.011281	-0.011089	-0.016929	-0.045816	-0.053590	-0.053316	-0.045451	-0.035120	-0.070589	-0.070589	0.028767	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.020227	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	0.004397	-0.035894	-0.035894	-0.035894	0.220902	0.027417	-0.043204	-0.045367	0.197200	0.059346	-1.314264e-02	0.726549	-0.018630	0.152457	0.050596

pred_class_train	0	1
actual
0	61453	1407
1	2689	4450

pred_class_test	0	1
actual
0	26299	649
1	1202	1850

	min_arpu	min_onnet_mou	min_ofnet_mou	min_roam_ic_mou	min_roam_og_mou	min_loc_og_t2t_mou	min_loc_og_t2m_mou	min_loc_og_t2f_mou	min_loc_og_t2c_mou	min_loc_og_mou	min_std_og_t2t_mou	min_std_og_t2m_mou	min_std_og_t2f_mou	min_std_og_t2c_mou	min_isd_og_mou	min_std_og_mou	min_spl_og_mou	min_og_others	min_total_og_mou	min_loc_ic_t2t_mou	min_loc_ic_t2m_mou	min_loc_ic_t2f_mou	min_loc_ic_mou	min_std_ic_t2t_mou	min_std_ic_t2m_mou	min_std_ic_t2f_mou	min_std_ic_t2o_mou	min_std_ic_mou	min_total_ic_mou	min_spl_ic_mou	min_isd_ic_mou	min_ic_others	min_total_rech_num	min_total_rech_amt	min_max_rech_amt	min_last_day_rch_amt	min_vol_2g_mb	min_vol_3g_mb	min_monthly_2g	min_sachet_2g	min_monthly_3g	min_sachet_3g	min_vbc_3g	max_arpu	max_onnet_mou	max_ofnet_mou	max_roam_ic_mou	max_roam_og_mou	max_loc_og_t2t_mou	max_loc_og_t2m_mou	max_loc_og_t2f_mou	max_loc_og_t2c_mou	max_loc_og_mou	max_std_og_t2t_mou	max_std_og_t2m_mou	max_std_og_t2f_mou	max_std_og_t2c_mou	max_isd_og_mou	max_std_og_mou	max_spl_og_mou	max_og_others	max_total_og_mou	max_loc_ic_t2t_mou	max_loc_ic_t2m_mou	max_loc_ic_t2f_mou	max_loc_ic_mou	max_std_ic_t2t_mou	max_std_ic_t2m_mou	max_std_ic_t2f_mou	max_std_ic_t2o_mou	max_std_ic_mou	max_total_ic_mou	max_spl_ic_mou	max_isd_ic_mou	max_ic_others	max_total_rech_num	max_total_rech_amt	max_max_rech_amt	max_last_day_rch_amt	max_vol_2g_mb	max_vol_3g_mb	max_monthly_2g	max_sachet_2g	max_monthly_3g	max_sachet_3g	max_vbc_3g	sum_arpu	sum_onnet_mou	sum_ofnet_mou	sum_roam_ic_mou	sum_roam_og_mou	sum_loc_og_t2t_mou	sum_loc_og_t2m_mou	sum_loc_og_t2f_mou	sum_loc_og_t2c_mou	sum_loc_og_mou	sum_std_og_t2t_mou	sum_std_og_t2m_mou	sum_std_og_t2f_mou	sum_std_og_t2c_mou	sum_isd_og_mou	sum_std_og_mou	sum_spl_og_mou	sum_og_others	sum_total_og_mou	sum_loc_ic_t2t_mou	sum_loc_ic_t2m_mou	sum_loc_ic_t2f_mou	sum_loc_ic_mou	sum_std_ic_t2t_mou	sum_std_ic_t2m_mou	sum_std_ic_t2f_mou	sum_std_ic_t2o_mou	sum_std_ic_mou	sum_total_ic_mou	sum_spl_ic_mou	sum_isd_ic_mou	sum_ic_others	sum_total_rech_num	sum_total_rech_amt	sum_max_rech_amt	sum_last_day_rch_amt	sum_total_rech_data	sum_max_rech_data	sum_count_rech_2g	sum_count_rech_3g	sum_av_rech_amt_data	sum_vol_2g_mb	sum_vol_3g_mb	sum_arpu_3g	sum_arpu_2g	sum_night_pck_user	sum_monthly_2g	sum_sachet_2g	sum_monthly_3g	sum_sachet_3g	sum_fb_user	sum_vbc_3g	mean_arpu	mean_onnet_mou	mean_ofnet_mou	mean_roam_ic_mou	mean_roam_og_mou	mean_loc_og_t2t_mou	mean_loc_og_t2m_mou	mean_loc_og_t2f_mou	mean_loc_og_t2c_mou	mean_loc_og_mou	mean_std_og_t2t_mou	mean_std_og_t2m_mou	mean_std_og_t2f_mou	mean_std_og_t2c_mou	mean_isd_og_mou	mean_std_og_mou	mean_spl_og_mou	mean_og_others	mean_total_og_mou	mean_loc_ic_t2t_mou	mean_loc_ic_t2m_mou	mean_loc_ic_t2f_mou	mean_loc_ic_mou	mean_std_ic_t2t_mou	mean_std_ic_t2m_mou	mean_std_ic_t2f_mou	mean_std_ic_t2o_mou	mean_std_ic_mou	mean_total_ic_mou	mean_spl_ic_mou	mean_isd_ic_mou	mean_ic_others	mean_total_rech_num	mean_total_rech_amt	mean_max_rech_amt	mean_last_day_rch_amt	mean_vol_2g_mb	mean_vol_3g_mb	mean_monthly_2g	mean_sachet_2g	mean_monthly_3g	mean_sachet_3g	mean_vbc_3g	mom_arpu	mom_onnet_mou	mom_ofnet_mou	mom_loc_og_t2t_mou	mom_loc_og_t2m_mou	mom_loc_og_mou	mom_total_og_mou	mom_loc_ic_t2t_mou	mom_loc_ic_t2m_mou	mom_loc_ic_mou	mom_std_ic_mou	mom_total_ic_mou	mom_total_rech_num	mom_total_rech_amt	mom_max_rech_amt	mom_last_day_rch_amt	moq_arpu	moq_onnet_mou	moq_ofnet_mou	moq_loc_og_t2t_mou	moq_loc_og_t2m_mou	moq_loc_og_mou	moq_total_og_mou	moq_loc_ic_t2t_mou	moq_loc_ic_t2m_mou	moq_loc_ic_mou	moq_total_ic_mou	moq_total_rech_num	moq_total_rech_amt	moq_max_rech_amt	arpu_inlast	onnet_mou_inlast	ofnet_mou_inlast	roam_ic_mou_inlast	roam_og_mou_inlast	loc_og_t2t_mou_inlast	loc_og_t2m_mou_inlast	loc_og_t2f_mou_inlast	loc_og_t2c_mou_inlast	loc_og_mou_inlast	std_og_t2t_mou_inlast	std_og_t2m_mou_inlast	std_og_t2f_mou_inlast	std_og_t2c_mou_inlast	std_og_mou_inlast	isd_og_mou_inlast	spl_og_mou_inlast	og_others_inlast	total_og_mou_inlast	loc_ic_t2t_mou_inlast	loc_ic_t2m_mou_inlast	loc_ic_t2f_mou_inlast	loc_ic_mou_inlast	std_ic_t2t_mou_inlast	std_ic_t2m_mou_inlast	std_ic_t2f_mou_inlast	std_ic_t2o_mou_inlast	std_ic_mou_inlast	total_ic_mou_inlast	spl_ic_mou_inlast	isd_ic_mou_inlast	ic_others_inlast	total_rech_num_inlast	total_rech_amt_inlast	max_rech_amt_inlast	last_day_rch_amt_inlast	vol_2g_mb_inlast	vol_3g_mb_inlast	monthly_2g_inlast	sachet_2g_inlast	monthly_3g_inlast	sachet_3g_inlast	aug_vbc_3g_inlast
min_arpu	1.000000	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	0.676357	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	0.335704	-0.055562	-0.055562	-0.055562	4.996630e-01	0.964394	0.589338	0.376537	0.076647	0.232448	0.087483	0.052473	0.224248	0.066380	0.211992	0.768409	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	0.490560	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	0.258845	-0.055562	-0.055562	-0.055562	0.306744	0.765558	0.337101	0.327023	0.074490	0.225005	0.093971	0.059940	0.230074	0.078414	0.208438	0.919062	0.389736	0.560122	0.079568	0.204610	0.252485	0.459546	0.218918	0.052717	0.455006	0.291995	0.313301	0.100733	NaN	0.414327	0.394516	0.108297	0.034910	0.602305	0.134095	0.282906	0.100491	0.276574	0.100399	0.113289	0.054512	NaN	0.142973	0.305063	-0.022876	0.074368	0.029725	0.411452	0.908136	0.506333	0.437871	0.142060	0.257449	0.078560	0.193912	0.286046	0.083412	0.244936	0.294309	0.280538	0.044235	0.107359	0.060734	0.251539	0.080560	0.199310	0.226214	0.919062	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	0.602305	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	-0.055562	0.305063	-0.055562	-0.055562	-0.055562	0.411452	0.908136	0.506333	0.437871	0.083412	0.244936	0.107359	0.060734	0.251539	0.080560	0.226214	-0.092730	-0.208598	-0.186376	-0.228884	-0.204923	-0.200443	-0.134992	-0.209751	-0.193989	-0.188221	-0.247959	-0.126364	-0.062342	-0.156941	-0.156941	-0.235389	-0.172954	-0.258036	-0.233339	-0.265356	-0.247850	-0.244245	-0.193490	-0.238056	-0.223823	-0.221250	-0.177627	-0.116583	-0.241174	-0.241174	0.827901	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	0.530158	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	-0.153633	0.291950	-0.153633	-0.153633	-0.153633	0.335493	0.806694	0.433091	0.310998	0.069492	0.225912	8.771400e-02	0.065082	0.221977	0.067174	0.203261
min_onnet_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338554e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067047	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665636e-02	-0.023631	0.055777	0.000891	-0.004021
min_ofnet_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338551e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048796	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048796	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067047	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_roam_ic_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_roam_og_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_loc_og_t2t_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338560e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_loc_og_t2m_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338558e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665634e-02	-0.023631	0.055777	0.000891	-0.004021
min_loc_og_t2f_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_loc_og_t2c_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_loc_og_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338556e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057099	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286523	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067047	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665634e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_og_t2t_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338556e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067047	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665636e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_og_t2m_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338556e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067047	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665636e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_og_t2f_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_og_t2c_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_isd_og_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_og_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338549e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080153	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049396	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067047	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665636e-02	-0.023631	0.055777	0.000891	-0.004021
min_spl_og_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_og_others	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_total_og_mou	0.676357	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	1.000000	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	0.298090	-0.059790	-0.059790	-0.059790	4.736177e-01	0.663686	0.357694	0.282474	-0.007357	0.021019	-0.001772	-0.010274	0.028477	0.002716	0.034017	0.503449	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	0.715438	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	0.207144	-0.059790	-0.059790	-0.059790	0.293736	0.499377	0.198956	0.207562	-0.020543	0.011914	-0.011347	-0.002127	0.029556	-0.001595	0.017032	0.614246	0.652848	0.689267	-0.006559	0.027606	0.419227	0.478667	0.151966	0.080777	0.564025	0.516434	0.524839	0.072250	NaN	0.071311	0.676568	0.141954	0.033697	0.877865	0.129756	0.230458	0.071802	0.234941	0.098772	0.106905	0.033457	NaN	0.134267	0.255798	-0.009221	0.024956	0.021932	0.391827	0.610221	0.301909	0.292500	-0.000101	0.015589	-0.007698	0.016781	0.017957	-0.017103	0.017160	0.038087	0.033020	0.024218	-0.007908	-0.006414	0.033077	0.000038	-0.013050	0.024914	0.614246	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	0.877865	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	-0.059790	0.255798	-0.059790	-0.059790	-0.059790	0.391827	0.610221	0.301909	0.292500	-0.017103	0.017160	-0.007908	-0.006414	0.033077	0.000038	0.024914	-0.073885	-0.204506	-0.172369	-0.229647	-0.203596	-0.200684	-0.146153	-0.199603	-0.180715	-0.177637	-0.240396	-0.129278	-0.048101	-0.126719	-0.126719	-0.195523	-0.137694	-0.249228	-0.213664	-0.265157	-0.246690	-0.245922	-0.202929	-0.228751	-0.216284	-0.214166	-0.180034	-0.087935	-0.193325	-0.193325	0.546049	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	0.770610	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	-0.135221	0.244973	-0.135221	-0.135221	-0.135221	0.324901	0.526085	0.259057	0.201586	-0.011029	0.020431	-7.192783e-03	0.009023	0.031398	0.000492	0.025217
min_loc_ic_t2t_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_loc_ic_t2m_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338559e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286523	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665634e-02	-0.023631	0.055777	0.000891	-0.004021
min_loc_ic_t2f_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_loc_ic_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338558e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080716	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033522	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286523	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665634e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_ic_t2t_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_ic_t2m_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_ic_t2f_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_ic_t2o_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_std_ic_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072415	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_total_ic_mou	0.335704	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	0.298090	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	1.000000	-0.067235	-0.067235	-0.067235	1.932729e-01	0.311132	0.277172	0.199727	0.019946	0.062818	0.061683	0.006526	0.062735	0.009841	0.099481	0.205413	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	0.138628	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	0.810716	-0.067235	-0.067235	-0.067235	0.060802	0.208597	0.159218	0.157370	-0.000561	0.049181	0.063978	-0.002730	0.068279	0.004831	0.078245	0.277159	0.087705	0.224308	-0.007777	-0.039661	0.230359	0.426904	0.208992	0.017728	0.420466	-0.031211	-0.002343	0.084505	NaN	0.021251	-0.019977	0.034384	0.020360	0.212294	0.592536	0.721878	0.355216	0.858299	0.226325	0.314546	0.170617	NaN	0.373819	0.926438	-0.009584	0.247076	0.127384	0.122056	0.270575	0.238848	0.211459	0.027777	0.105663	0.013319	0.042438	0.091101	0.008821	0.059343	0.081226	0.072747	-0.005018	0.073470	0.000503	0.073219	0.006490	0.091582	0.092841	0.277159	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	0.212294	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	-0.067235	0.926438	-0.067235	-0.067235	-0.067235	0.122056	0.270575	0.238848	0.211459	0.008821	0.059343	0.073470	0.000503	0.073219	0.006490	0.092841	-0.079506	-0.207784	-0.186910	-0.246115	-0.220509	-0.216441	-0.157271	-0.236623	-0.205726	-0.202242	-0.202406	-0.148286	-0.053556	-0.122679	-0.122679	-0.160902	-0.143732	-0.238755	-0.220562	-0.278202	-0.260935	-0.258428	-0.212657	-0.276033	-0.249222	-0.244297	-0.204781	-0.079146	-0.172337	-0.172337	0.246415	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	0.180827	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	-0.152059	0.848240	-0.152059	-0.152059	-0.152059	0.082746	0.236127	0.204889	0.166513	0.005950	0.056111	5.854484e-02	0.006389	0.066374	0.004127	0.084863
min_spl_ic_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_isd_ic_mou	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338561e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_ic_others	-0.055562	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.059790	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.067235	1.000000	1.000000	1.000000	-8.338562e-02	-0.050630	-0.028164	-0.020047	-0.011736	0.030278	-0.012670	-0.014647	0.057098	0.006912	-0.011068	-0.029479	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080154	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.080717	1.000000	1.000000	1.000000	-0.089872	-0.042091	0.036584	0.039007	-0.019466	0.056090	-0.020589	-0.030128	0.075557	-0.001217	0.009281	-0.048797	-0.049947	-0.070040	-0.013204	-0.022103	-0.034213	-0.064626	-0.030578	-0.026749	-0.063199	-0.036438	-0.039250	-0.015828	NaN	-0.003263	-0.049397	-0.032937	-0.008546	-0.076634	-0.037976	-0.070820	-0.033523	-0.072970	-0.020786	-0.029505	-0.015329	NaN	-0.034748	-0.078517	-0.044263	-0.014815	-0.009210	-0.095865	-0.053103	0.004259	0.016143	-0.009182	0.088909	-0.028858	0.040119	0.058335	-0.017890	0.048881	0.074760	0.082874	-0.007190	-0.019813	-0.025787	0.076303	0.001793	0.024556	0.000030	-0.048797	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.076634	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	-0.078517	1.000000	1.000000	1.000000	-0.095865	-0.053103	0.004259	0.016143	-0.017890	0.048881	-0.019813	-0.025787	0.076303	0.001793	0.000030	0.286524	0.297717	0.351847	0.250122	0.299801	0.308106	0.429595	0.280532	0.332872	0.340792	0.206047	0.471742	0.333947	0.212688	0.212688	0.128976	0.209126	0.228027	0.276590	0.190953	0.230525	0.238672	0.294634	0.215942	0.264740	0.274328	0.328325	0.202884	0.146530	0.146530	-0.051934	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.067048	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	0.442165	-0.072416	0.442165	0.442165	0.442165	-0.085190	-0.051907	-0.009221	0.000688	-0.017071	0.038168	-1.665635e-02	-0.023631	0.055777	0.000891	-0.004021
min_total_rech_num	0.499663	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083385	-0.083386	-0.083386	0.473618	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	0.193273	-0.083386	-0.083386	-0.083386	1.000000e+00	0.512838	0.087268	0.056482	0.090565	0.076861	-0.005156	0.283671	0.024433	0.135066	0.046977	0.348330	-0.083385	-0.083385	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083385	-0.083386	-0.083386	0.358727	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	0.148199	-0.083386	-0.083386	-0.083386	0.718878	0.325333	-0.099331	-0.113954	0.085540	0.072212	-0.006611	0.280629	0.024557	0.160573	0.047284	0.442386	0.310982	0.359569	0.017414	0.069273	0.146902	0.230918	-0.000175	0.095930	0.234734	0.272876	0.280266	-0.016791	NaN	0.049168	0.358312	0.164119	0.053224	0.434291	0.086578	0.173790	0.015271	0.164868	0.067865	0.069469	-0.011685	NaN	0.084152	0.176126	0.053124	0.017354	0.021330	0.885770	0.435979	-0.031525	-0.063586	0.311724	0.020913	0.297023	0.149534	0.116155	0.097345	0.080270	0.062408	0.068898	0.097307	-0.005048	0.302450	0.028002	0.163884	0.152619	0.051591	0.442386	-0.083386	-0.083385	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083385	-0.083386	-0.083386	0.434291	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	-0.083386	0.176126	-0.083386	-0.083386	-0.083386	0.885770	0.435979	-0.031525	-0.063586	0.097345	0.080270	-0.005048	0.302450	0.028002	0.163884	0.051591	-0.114484	-0.220674	-0.207499	-0.229592	-0.221458	-0.217781	-0.161142	-0.217649	-0.207361	-0.204912	-0.211237	-0.150983	-0.089420	-0.187653	-0.187653	-0.269312	-0.179218	-0.255304	-0.243695	-0.248906	-0.251668	-0.250337	-0.206045	-0.230207	-0.231017	-0.229084	-0.188925	-0.189110	-0.278238	-0.278238	0.404814	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	0.390963	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	-0.175256	0.175429	-0.175256	-0.175256	-0.175256	0.771709	0.394760	-0.007570	-0.042457	0.085833	0.080892	9.335822e-07	0.276418	0.030137	0.144431	0.052325
min_total_rech_amt	0.964394	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	0.663686	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	0.311132	-0.050630	-0.050630	-0.050630	5.128384e-01	1.000000	0.612488	0.391196	0.077596	0.225374	0.085900	0.055809	0.213963	0.066707	0.196871	0.758824	-0.050630	-0.050629	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	0.490914	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	0.241335	-0.050630	-0.050630	-0.050630	0.320943	0.749758	0.279095	0.294792	0.077604	0.217056	0.092131	0.066736	0.216601	0.079979	0.195840	0.900323	0.389971	0.552282	0.074217	0.202029	0.241237	0.439401	0.181828	0.055309	0.433458	0.298686	0.319302	0.084200	NaN	0.418362	0.402412	0.106030	0.032260	0.597435	0.126610	0.263487	0.081806	0.256671	0.095212	0.106509	0.044561	NaN	0.133839	0.283693	-0.018102	0.070091	0.026300	0.425561	0.910293	0.470941	0.422077	0.144438	0.244316	0.084080	0.187848	0.275737	0.085757	0.236672	0.279032	0.266707	0.045600	0.105541	0.066660	0.237719	0.081692	0.195075	0.211612	0.900323	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	0.597435	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	-0.050630	0.283693	-0.050630	-0.050630	-0.050630	0.425561	0.910293	0.470941	0.422077	0.085757	0.236672	0.105541	0.066660	0.237719	0.081692	0.211612	-0.084322	-0.189506	-0.169274	-0.209849	-0.186872	-0.182685	-0.122945	-0.193848	-0.178182	-0.172553	-0.236278	-0.115955	-0.058937	-0.156761	-0.156761	-0.248103	-0.157156	-0.236348	-0.212333	-0.244965	-0.227537	-0.224301	-0.178373	-0.221221	-0.207859	-0.204847	-0.165060	-0.124968	-0.256104	-0.256104	0.815102	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	0.531203	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	-0.140188	0.273486	-0.140188	-0.140188	-0.140188	0.351024	0.815077	0.412798	0.311263	0.072159	0.219267	8.685290e-02	0.069704	0.210436	0.068167	0.190717
min_max_rech_amt	0.589338	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	0.357694	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	0.277172	-0.028164	-0.028164	-0.028164	8.726779e-02	0.612488	1.000000	0.487148	0.101765	0.302275	0.193625	-0.023402	0.290054	0.016342	0.276263	0.474339	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	0.232218	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	0.208173	-0.028164	-0.028164	-0.028164	-0.044900	0.482793	0.494463	0.520681	0.091985	0.285956	0.171054	-0.029632	0.280020	0.020607	0.275612	0.558551	0.181445	0.303717	0.068567	0.143148	0.179499	0.347191	0.269504	0.014837	0.342628	0.093339	0.106661	0.140209	NaN	0.121038	0.132510	0.021411	0.007710	0.302969	0.104736	0.228018	0.129295	0.228709	0.068104	0.088986	0.071469	NaN	0.111192	0.248562	-0.050262	0.057126	0.017101	0.009291	0.574143	0.789696	0.652110	0.072559	0.426078	0.006338	0.173856	0.366991	0.105037	0.313653	0.357452	0.344143	0.010496	0.206549	-0.030168	0.309093	0.020990	0.269283	0.297140	0.558551	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	0.302969	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	-0.028164	0.248562	-0.028164	-0.028164	-0.028164	0.009291	0.574143	0.789696	0.652110	0.105037	0.313653	0.206549	-0.030168	0.309093	0.020990	0.297140	-0.099689	-0.169072	-0.156870	-0.190860	-0.173393	-0.170008	-0.098859	-0.188112	-0.176466	-0.174743	-0.192081	-0.108842	-0.069636	-0.186279	-0.186279	-0.217779	-0.185157	-0.220869	-0.204086	-0.229751	-0.217366	-0.215166	-0.162698	-0.219075	-0.210583	-0.210074	-0.165613	-0.147536	-0.302355	-0.302355	0.506199	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	0.270079	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	-0.142786	0.239982	-0.142786	-0.142786	-0.142786	-0.009632	0.516607	0.675003	0.509713	0.082684	0.282508	1.686046e-01	-0.017247	0.268681	0.013476	0.263016
min_last_day_rch_amt	0.376537	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	0.282474	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	0.199727	-0.020047	-0.020047	-0.020047	5.648191e-02	0.391196	0.487148	1.000000	0.032742	0.168853	0.070315	-0.023268	0.165662	0.007618	0.129967	0.302555	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	0.186680	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	0.143195	-0.020047	-0.020047	-0.020047	-0.027573	0.301572	0.223366	0.328548	0.024709	0.144829	0.071690	-0.049566	0.153123	0.002196	0.120230	0.355913	0.147303	0.241481	0.035254	0.123654	0.127435	0.286173	0.182864	-0.000331	0.267905	0.083580	0.088560	0.093024	NaN	0.038419	0.113479	0.009760	-0.006396	0.241591	0.075623	0.177210	0.079764	0.171317	0.031792	0.045259	0.043336	NaN	0.056172	0.174593	-0.033692	0.033996	0.009835	0.007219	0.363362	0.372349	0.634567	0.011263	0.191601	-0.027856	0.090842	0.185127	0.030796	0.165051	0.200369	0.193495	-0.010511	0.082346	-0.042874	0.172715	0.004095	0.104608	0.133998	0.355913	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	0.241591	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	-0.020047	0.174593	-0.020047	-0.020047	-0.020047	0.007219	0.363362	0.372349	0.634567	0.030796	0.165051	0.082346	-0.042874	0.172715	0.004095	0.133998	-0.061144	-0.114053	-0.099325	-0.131219	-0.110525	-0.107738	-0.061763	-0.121883	-0.108561	-0.107215	-0.124129	-0.065687	-0.042782	-0.113029	-0.113029	-0.255072	-0.114311	-0.149352	-0.132351	-0.160676	-0.145165	-0.141774	-0.107673	-0.148006	-0.136299	-0.134640	-0.105604	-0.090800	-0.186082	-0.186082	0.333286	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	0.224944	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	-0.091610	0.171818	-0.091610	-0.091610	-0.091610	-0.009612	0.329150	0.323961	0.473784	0.025612	0.156178	6.580133e-02	-0.035692	0.148172	0.003064	0.128243
min_vol_2g_mb	0.076647	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.007357	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	0.019946	-0.011736	-0.011736	-0.011736	9.056481e-02	0.077596	0.101765	0.032742	1.000000	0.030777	0.323651	0.261002	0.039305	0.032165	0.081884	0.043891	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.030010	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	0.010275	-0.011736	-0.011736	-0.011736	0.052094	0.040609	0.031369	0.016319	0.623823	0.039355	0.265939	0.235053	0.056148	0.054655	0.086722	0.061404	-0.023527	-0.012073	-0.000815	-0.003175	0.001651	0.002791	-0.003084	0.019184	0.002595	-0.028106	-0.017271	-0.002119	NaN	-0.001307	-0.029429	0.016924	0.001655	-0.022674	0.008832	0.014859	0.010504	0.016330	-0.000619	0.001912	0.002444	NaN	0.001491	0.014411	-0.007671	-0.001356	0.003242	0.071953	0.060244	0.068357	0.028520	0.295923	0.245127	0.315964	0.066678	0.262129	0.813194	0.038860	0.051884	0.087788	0.078030	0.331902	0.261969	0.053145	0.048148	0.348262	0.090612	0.061404	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.022674	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	-0.011736	0.014411	-0.011736	-0.011736	-0.011736	0.071953	0.060244	0.068357	0.028520	0.813194	0.038860	0.331902	0.261969	0.053145	0.048148	0.090612	-0.021083	0.021850	-0.004523	0.013409	0.005328	0.005893	0.008635	-0.024635	-0.030025	-0.031518	0.009014	-0.020626	-0.015355	-0.039345	-0.039345	-0.048842	-0.039799	0.014207	-0.004109	0.003352	0.000767	-0.000378	-0.001181	-0.027856	-0.037250	-0.039074	-0.033774	-0.032392	-0.063195	-0.063195	0.054542	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.018129	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	-0.031426	0.016606	-0.031426	-0.031426	-0.031426	0.058414	0.052828	0.053464	0.018060	0.671698	0.034701	2.673638e-01	0.220654	0.042999	0.041474	0.081104
min_vol_3g_mb	0.232448	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.021019	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.062818	0.030278	0.030278	0.030278	7.686079e-02	0.225374	0.302275	0.168853	0.030777	1.000000	0.186316	0.069230	0.596986	0.234633	0.566077	0.209462	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	-0.009302	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.046640	0.030278	0.030278	0.030278	0.032231	0.200523	0.191708	0.171777	0.047174	0.737854	0.169326	0.067212	0.496777	0.209536	0.519336	0.231960	-0.005590	0.010451	0.009346	0.006528	0.028703	0.056610	0.039093	0.006914	0.055262	-0.023977	-0.027256	0.010774	NaN	0.003441	-0.033012	0.004682	-0.002708	0.002544	0.021165	0.050939	0.031128	0.050226	0.016865	0.012750	0.019992	NaN	0.020886	0.055304	-0.028787	0.022172	0.004368	0.053781	0.225440	0.272401	0.211950	0.280561	0.602773	0.106558	0.490561	0.721084	0.043110	0.884085	0.711248	0.719830	0.119795	0.201882	0.072409	0.580825	0.237844	0.380896	0.573230	0.231960	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.002544	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.030278	0.055304	0.030278	0.030278	0.030278	0.053781	0.225440	0.272401	0.211950	0.043110	0.884085	0.201882	0.072409	0.580825	0.237844	0.573230	-0.019448	0.032433	0.021705	0.018231	0.020539	0.021257	0.033351	-0.007812	-0.009696	-0.009697	0.002193	0.006982	-0.016749	-0.037563	-0.037563	-0.039321	-0.038388	0.014049	0.008473	-0.000587	0.001065	0.002904	0.006543	-0.023338	-0.021736	-0.022660	-0.015598	-0.035579	-0.063498	-0.063498	0.201689	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	0.004060	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	-0.006938	0.054201	-0.006938	-0.006938	-0.006938	0.036448	0.192746	0.219319	0.149561	0.033435	0.774063	1.619486e-01	0.063467	0.482757	0.188229	0.481688
min_monthly_2g	0.087483	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.001772	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	0.061683	-0.012670	-0.012670	-0.012670	-5.156182e-03	0.085900	0.193625	0.070315	0.323651	0.186316	1.000000	-0.022098	-0.022708	-0.007935	0.212343	0.042832	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.030597	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	0.049863	-0.012670	-0.012670	-0.012670	-0.040424	0.041807	0.065560	0.060236	0.265079	0.156887	0.516972	-0.026537	-0.014414	-0.015059	0.194950	0.064437	-0.025897	-0.008310	-0.003835	-0.012511	0.014331	0.032878	0.028973	-0.006430	0.031025	-0.037982	-0.030900	0.007364	NaN	-0.000426	-0.044490	-0.011706	-0.006994	-0.020894	0.031717	0.048840	0.048108	0.057290	0.005417	0.009656	0.010953	NaN	0.011558	0.056903	-0.024521	0.016782	0.003290	-0.028460	0.063582	0.132024	0.081493	0.081141	0.350145	0.105072	-0.022537	0.241434	0.313045	0.179186	-0.006640	-0.009329	-0.001599	0.759557	-0.027926	-0.021697	-0.013985	0.366056	0.216033	0.064437	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.020894	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	-0.012670	0.056903	-0.012670	-0.012670	-0.012670	-0.028460	0.063582	0.132024	0.081493	0.313045	0.179186	0.759557	-0.027926	-0.021697	-0.013985	0.216033	-0.019869	0.016038	-0.000588	0.000912	-0.003634	-0.003168	0.011204	-0.033834	-0.039880	-0.039335	-0.003704	-0.027846	-0.016171	-0.039264	-0.039264	-0.036851	-0.038799	0.001676	-0.003521	-0.013818	-0.012654	-0.012455	-0.000007	-0.044168	-0.048020	-0.048359	-0.040651	-0.034352	-0.064591	-0.064591	0.060108	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.015914	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	-0.030379	0.054148	-0.030379	-0.030379	-0.030379	-0.031623	0.059045	0.107749	0.060981	0.246176	0.163155	6.278501e-01	-0.025757	-0.018057	-0.013190	0.199682
min_sachet_2g	0.052473	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.010274	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	0.006526	-0.014647	-0.014647	-0.014647	2.836708e-01	0.055809	-0.023402	-0.023268	0.261002	0.069230	-0.022098	1.000000	-0.021589	0.140426	0.052414	0.012608	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.031108	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	0.000318	-0.014647	-0.014647	-0.014647	0.209661	0.008396	-0.059612	-0.067855	0.199639	0.059688	-0.015895	0.698883	-0.019143	0.173477	0.052223	0.031717	-0.015979	-0.024290	-0.008613	-0.017378	0.008315	-0.012168	-0.024369	0.024526	-0.004010	-0.022390	-0.019377	-0.015055	NaN	-0.002411	-0.027371	0.026492	0.005919	-0.024234	0.002473	0.011406	-0.005064	0.008107	-0.004258	-0.006663	-0.010202	NaN	-0.008623	0.003291	-0.002293	-0.002985	0.000378	0.256594	0.030555	-0.054818	-0.064195	0.770706	0.039284	0.841312	0.132892	0.235866	0.241053	0.067269	0.016247	0.029566	0.080134	-0.022360	0.858112	-0.022696	0.174819	0.344206	0.056544	0.031717	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.024234	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	-0.014647	0.003291	-0.014647	-0.014647	-0.014647	0.256594	0.030555	-0.054818	-0.064195	0.241053	0.067269	-0.022360	0.858112	-0.022696	0.174819	0.056544	-0.023418	0.015925	-0.017745	0.000432	-0.007395	-0.007419	-0.011987	-0.042014	-0.045157	-0.045539	0.015337	-0.032754	-0.016532	-0.043026	-0.043026	-0.066711	-0.043640	0.010071	-0.016432	-0.006464	-0.011281	-0.011089	-0.016929	-0.045816	-0.053590	-0.053316	-0.045451	-0.035120	-0.070589	-0.070589	0.028767	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.020227	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	-0.035894	0.004397	-0.035894	-0.035894	-0.035894	0.220902	0.027417	-0.043204	-0.045367	0.197200	0.059346	-1.314264e-02	0.726549	-0.018630	0.152457	0.050596